Paper Citation Record · LEDGER

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

As of 18 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 5 inbound Pith citation observations for arXiv:2501.04004.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.04004 v2

Coverage vector

measured 100 of 100 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:48:05.541991Z

measured 105 of 105 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:40:06.676272Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T23:39:03.882338Z

Reference resolution

100 of 100 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6316c9d4-8aec-481d-9923-023a161d6675 · outbound

This paper cites Slic superpix- els compared to state-of-the-art superpixel methods.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Slic superpix- els compared to state-of-the-art superpixel methods

Reference 1

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Observation 33b94fe3-d511-4439-add5-557b58ad9818 · outbound

This paper cites Se- mantickitti: A dataset for semantic scene understanding of lidar sequences.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Se- mantickitti: A dataset for semantic scene understanding of lidar sequences

Reference 2

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Observation 5adc496a-8f7e-4233-921c-5f1fb87e0471 · outbound

This paper cites Rep- resentation learning: A review and new perspectives.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rep- resentation learning: A review and new perspectives

Reference 3

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Observation b98958f8-6495-411d-afa1-9fd29332eb5c · outbound

This paper cites The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

Reference 4

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Observation a90e9a03-8850-45cd-ae28-ef1c2d97b91d · outbound

This paper cites Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather

Reference 5

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Observation 11df5f26-caa4-463f-8e98-f0e78d3c542e · outbound

This paper cites Also: Automotive lidar self- supervision by occupancy estimation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Also: Automotive lidar self- supervision by occupancy estimation

Reference 6

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Observation ff6fd51e-009a-465f-bf51-04ea237e3fd5 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes nuscenes: A multi- modal dataset for autonomous driving

Reference 7

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Observation eddae40b-969e-4771-accc-552c74b3f372 · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes A Survey on Mixture of Experts in Large Language Models

Reference 8

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Observation bae04efc-62d3-4dd7-8389-d79931e95de9 · outbound

This paper cites Building a strong pre- training baseline for universal 3d large-scale perception.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Building a strong pre- training baseline for universal 3d large-scale perception

Reference 9

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Observation 5a2397e4-2a58-44ea-8ea6-d6a7b59ad207 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes End-to-end autonomous driving: Challenges and frontiers

Reference 10

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Observation 20d39a9f-f4a9-4542-87d6-b6746b448ea0 · outbound

This paper cites Polarstream: Streaming lidar object detection and segmentation with po- lar pillars.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Polarstream: Streaming lidar object detection and segmentation with po- lar pillars

Reference 11

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Observation 21d7ccfc-d0be-4f34-9c92-598288f6e358 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes A simple framework for contrastive learning of visual representations

Reference 12

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Observation 3502e962-617d-47ef-b20a-b75c395b0073 · outbound

This paper cites Adamv-moe: Adaptive multi-task vision mixture-of-experts.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Adamv-moe: Adaptive multi-task vision mixture-of-experts

Reference 13

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Observation 1254c85f-dd30-49d9-9089-a9c52857cf00 · outbound

This paper cites Patch-level rout- ing in mixture-of-experts is provably sample-efficient for convolutional neural networks.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Patch-level rout- ing in mixture-of-experts is provably sample-efficient for convolutional neural networks

Reference 14

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Observation 872bcc07-80e7-432c-8127-dcc82fb8214b · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes 4d spatio-temporal convnets: Minkowski convolutional neural networks

Reference 15

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Observation f108481c-27dd-4ec5-989e-bcb4025bad9d · outbound

This paper cites MMDetection3D: Open- MMLab next-generation platform for general 3D object detection.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes MMDetection3D: Open- MMLab next-generation platform for general 3D object detection

Reference 16

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Observation 67b7f336-218d-43e7-82e3-7dae946c8ac0 · outbound

This paper cites Spconv: Spatially sparse convolu- tion library.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Spconv: Spatially sparse convolu- tion library

Reference 17

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Observation 50ce3b86-45c2-44fa-9a43-414fec8e0cd6 · outbound

This paper cites Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds

Reference 18

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Source-reported events for the cited work

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Observation 243ae020-5aa9-4d4e-8b19-efaf44927dc5 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 19

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Observation 373d9da0-df6b-4568-9321-5af2c2f7abc9 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Glam: Efficient scaling of language models with mixture-of-experts

Reference 20

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Observation bb5a6c6a-2e1f-4ea7-9b1e-df75a569947c · outbound

This paper cites A Review of Sparse Expert Models in Deep Learning.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes A Review of Sparse Expert Models in Deep Learning

Reference 21

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Observation 7af2afce-9c73-4e98-b025-9871194cbfe7 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 22

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Observation 62cf7897-16bf-4bd4-976a-19ff1f0232de · outbound

This paper cites Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking

Reference 23

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Observation 72f33dbe-5c4c-4d4b-a411-a9cb618b6e8f · outbound

This paper cites Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:07.122331Z

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Observation 54b3a745-a509-4ea0-ad79-a1b23d136ada · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark 23 suite.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Are we ready for autonomous driving? the kitti vision benchmark 23 suite

Reference 25

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Observation c42ad76c-399b-4b90-8ec5-acdff9fd8c36 · outbound

This paper cites Is your hd map construc- tor reliable under sensor corruptions? In Advances in Neu- ral Information Processing Systems, 2024.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Is your hd map construc- tor reliable under sensor corruptions? In Advances in Neu- ral Information Processing Systems, 2024

Reference 26

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Observation c24c0923-a5a6-4f44-9227-34910d7232b7 · outbound

This paper cites Deep residual learning for image recognition.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Deep residual learning for image recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:07.065446Z

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Observation b9bd90fc-0590-4333-beab-f0214a295d09 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Momentum contrast for unsupervised visual rep- resentation learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:07.049049Z

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Observation 2ffec77d-a606-4a19-a210-09c3c7a41769 · outbound

This paper cites Masked autoencoders are scal- able vision learners.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Masked autoencoders are scal- able vision learners

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:07.031493Z

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Observation 491beec4-d2c9-462c-ba8e-4b3b5cf9b8b3 · outbound

This paper cites Lidar-based panoptic segmentation via dynamic shifting network.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Lidar-based panoptic segmentation via dynamic shifting network

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:07.011284Z

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Observation 4f19cd93-bdd2-43c3-93d8-f95b0fbdfbbc · outbound

This paper cites Unified 3d and 4d panoptic segmentation via dynamic shifting networks.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Unified 3d and 4d panoptic segmentation via dynamic shifting networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.989187Z

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Observation 2862d9f5-628a-4d1a-9d17-9c08eeeb7728 · outbound

This paper cites Randla-net: Efficient semantic segmentation of large-scale point clouds.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Randla-net: Efficient semantic segmentation of large-scale point clouds

Reference 32

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Observation a8a2e9c3-b6a0-41fb-853e-9c7eaf613622 · outbound

This paper cites Tutel: Adaptive mixture-of-experts at scale.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Tutel: Adaptive mixture-of-experts at scale

Reference 33

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Observation c515b68f-6911-4ed4-b60a-117fc9f9349f · outbound

This paper cites Rellis-3d dataset: Data, benchmarks and anal- ysis.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rellis-3d dataset: Data, benchmarks and anal- ysis

Reference 34

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Observation 4489d56f-be9d-45a6-b12a-deaed5a88969 · outbound

This paper cites M4oe: A foundation model for medical multimodal image segmentation with mixture of experts.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes M4oe: A foundation model for medical multimodal image segmentation with mixture of experts

Reference 35

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Observation fd811fb5-11c4-4f48-8c30-a7b6b4dacdfc · outbound

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LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Seg- ment anything

Reference 36

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Observation e43573eb-8fa8-49c0-8b68-6cb6e111b36e · outbound

This paper cites Daps3d: Domain adaptive projective segmen- tation of 3d lidar point clouds.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Daps3d: Domain adaptive projective segmen- tation of 3d lidar point clouds

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.882163Z

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Observation b16c026c-e880-4dc3-81e5-21c9fe0b8902 · outbound

This paper cites Rethinking range view representation for lidar segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rethinking range view representation for lidar segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.863275Z

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Observation bb3008a9-d90f-45ba-90e3-cd708407f4dc · outbound

This paper cites Robo3d: Towards robust and reliable 3d perception against corruptions.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Robo3d: Towards robust and reliable 3d perception against corruptions

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.846055Z

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Observation f419e10d-172a-4e85-8e70-068a1dd724ce · outbound

This paper cites Lasermix for semi-supervised lidar semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Lasermix for semi-supervised lidar semantic segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.829332Z

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Observation 880b5d1b-2af0-4fad-991d-78643ecabe4a · outbound

This paper cites The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

Reference 41

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This paper cites Multi-modal data-efficient 3d scene understanding for au- tonomous driving.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Multi-modal data-efficient 3d scene understanding for au- tonomous driving

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.811845Z

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Observation 37447219-6790-405e-ac56-bea3daef73f8 · outbound

This paper cites Rapid-seg: Range-aware pointwise distance distribution networks for 3d lidar segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rapid-seg: Range-aware pointwise distance distribution networks for 3d lidar segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.794508Z

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Observation 84d83784-60ee-482c-a637-c0be9281352a · outbound

This paper cites Exploring geometry-aware contrast and cluster- ing harmonization for self-supervised 3d object detection.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Exploring geometry-aware contrast and cluster- ing harmonization for self-supervised 3d object detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.777196Z

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Observation 2e1a3691-8a26-4124-a22d-9e5b2d664f8c · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 45

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Observation 73735166-a81d-4f92-acd6-0e534834912a · outbound

This paper cites AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation

Reference 46

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This paper cites Uniseg: A unified multi-modal li- dar segmentation network and the openpcseg codebase.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Uniseg: A unified multi-modal li- dar segmentation network and the openpcseg codebase

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.759067Z

Source-reported events for the cited work

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Observation e9eb5068-fe5c-417d-b45b-ff797505940b · outbound

This paper cites Seg- ment any point cloud sequences by distilling vision founda- tion models.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Seg- ment any point cloud sequences by distilling vision founda- tion models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.742509Z

Source-reported events for the cited work

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Observation f1af2d22-fb33-4da3-be83-e64b27adfab2 · outbound

This paper cites Multi- space alignments towards universal lidar segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Multi- space alignments towards universal lidar segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.724273Z

Source-reported events for the cited work

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Observation 88c8140f-b4e7-4a51-9d51-c1b72cafd2ea · outbound

This paper cites Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining

Reference 50

Resolution: verified exact; raw_fallback, observed 2026-08-10T21:48:05.858982Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8921a330-50b0-4e7f-8492-a1a6145a29b0 · outbound

This paper cites Decoupled weight de- cay regularization.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Decoupled weight de- cay regularization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.704123Z

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Observation 3c183bdc-6a8b-409c-a327-63b05516b26e · outbound

This paper cites Vision-centric bev perception: A survey.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Vision-centric bev perception: A survey

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.686715Z

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Observation 66351440-d099-41ea-96be-7f73540eb122 · outbound

This paper cites Self- supervised image-to-point distillation via semantically tol- erant contrastive loss.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Self- supervised image-to-point distillation via semantically tol- erant contrastive loss

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.669204Z

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Observation b46e3849-ad48-4bb5-bb15-076d7bbd3cbd · outbound

This paper cites Mixture of ex- perts: a literature survey.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Mixture of ex- perts: a literature survey

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.652276Z

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Observation 640002dc-4424-4ff1-84af-26a921256116 · outbound

This paper cites Rangenet++: Fast and accurate lidar semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rangenet++: Fast and accurate lidar semantic segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.636104Z

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Observation 7b0029ab-e52e-402f-8187-9e12512c6cd2 · outbound

This paper cites Deep learning for safe autonomous driving: Current challenges and future directions.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Deep learning for safe autonomous driving: Current challenges and future directions

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.301539Z

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Observation d0305046-477d-4d63-81ca-89114aa29673 · outbound

This paper cites Rectified linear units improve restricted boltzmann machines.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rectified linear units improve restricted boltzmann machines

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.609115Z

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Observation 14e73a70-0ac2-46d2-aef2-2330acc0945b · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes DINOv2: Learning Robust Visual Features without Supervision

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.311562Z

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Observation af651953-c367-40b7-a843-3e80a0f8ba96 · outbound

This paper cites Semanticposs: A point cloud dataset with large quantity of dynamic instances.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Semanticposs: A point cloud dataset with large quantity of dynamic instances

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.592258Z

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Observation 143af932-0cb2-4469-803e-5ace984bffd6 · outbound

This paper cites Unsupervised 3d point cloud representation learning by triangle constrained contrast for autonomous driving.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Unsupervised 3d point cloud representation learning by triangle constrained contrast for autonomous driving

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.575929Z

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Observation 2c354318-bcaf-4b02-867e-27a6c8a4e45e · outbound

This paper cites Using mixture of expert models to gain in- sights into semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Using mixture of expert models to gain in- sights into semantic segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.558498Z

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Observation 67b73b6b-44b0-435d-82b5-beb3fd152349 · outbound

This paper cites Perception, planning, control, and coordination for autonomous vehicles.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Perception, planning, control, and coordination for autonomous vehicles

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.542080Z

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Observation 5e879274-e1be-4282-b413-fa8512dfd3ef · outbound

This paper cites Using a waffle iron for automotive point cloud semantic segmenta- tion.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Using a waffle iron for automotive point cloud semantic segmenta- tion

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.524446Z

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Observation 9f8add5d-b229-41b4-8f8c-a6ffc9eaccfd · outbound

This paper cites Gfnet: Geo- metric flow network for 3d point cloud semantic segmenta- tion.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Gfnet: Geo- metric flow network for 3d point cloud semantic segmenta- tion

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.504213Z

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Observation 7aa87464-5990-423a-bc67-6d91c9df9c08 · outbound

This paper cites Lite-hdseg: Lidar semantic segmentation using lite harmonic dense convolutions.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Lite-hdseg: Lidar semantic segmentation using lite harmonic dense convolutions

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.486713Z

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Observation f9ee89e7-3449-4552-afc6-3e008b6ebb76 · outbound

This paper cites Scaling vision with sparse mix- ture of experts.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Scaling vision with sparse mix- ture of experts

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.469282Z

Source-reported events for the cited work

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Observation 45866e8a-97f1-453c-a228-a172eed0b1c8 · outbound

This paper cites Gipso: Geometrically informed propa- gation for online adaptation in 3d lidar segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Gipso: Geometrically informed propa- gation for online adaptation in 3d lidar segmentation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.451822Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87670f67-58bc-43cc-96c8-375dd3073202 · outbound

This paper cites Image-to-lidar self-supervised distillation for autonomous driving data.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Image-to-lidar self-supervised distillation for autonomous driving data

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.435167Z

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Observation 27c98918-65d6-4b45-92db-dcbb5bdee790 · outbound

This paper cites Bevcontrast: Self-supervision in bev space for automotive lidar point clouds.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Bevcontrast: Self-supervision in bev space for automotive lidar point clouds

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.369381Z

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Observation 4c1689b6-4b27-4e62-be3e-003ece92ef1b · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.374497Z

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Observation 349336cd-f634-44ae-9f52-8fa76f53b3cc · outbound

This paper cites Backward atten- tive fusing network with local aggregation classifier for 3d point cloud semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Backward atten- tive fusing network with local aggregation classifier for 3d point cloud semantic segmentation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.406529Z

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Observation 769aad85-7bb4-454d-8d60-91db2e0f029a · outbound

This paper cites Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.385269Z

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Observation c3cf959e-a743-4d3a-8216-58aace8290bb · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Scalability in perception for autonomous driving: Waymo open dataset

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.390064Z

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Observation 39cbaf81-b67d-47f0-9863-10e56436e14e · outbound

This paper cites Searching efficient 3d architectures with sparse point-voxel convolution.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Searching efficient 3d architectures with sparse point-voxel convolution

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.373365Z

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Observation fdf8fd19-1f22-4160-a6ba-3922931f54d1 · outbound

This paper cites Torchsparse: Efficient point cloud inference engine.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Torchsparse: Efficient point cloud inference engine

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.356647Z

Source-reported events for the cited work

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Observation acc27b7c-bee4-462a-91dd-d2c62001d24c · outbound

This paper cites Torchsparse++: Efficient training and inference framework for sparse convolution on gpus.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Torchsparse++: Efficient training and inference framework for sparse convolution on gpus

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.340129Z

Source-reported events for the cited work

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Observation dd744fe2-5fd4-47b1-ae48-a02e4692bc81 · outbound

This paper cites Scribble- supervised lidar semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Scribble- supervised lidar semantic segmentation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.324317Z

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Observation e46640b1-0d19-4c64-b40d-b9a09d9accbe · outbound

This paper cites Residual Mixture of Experts.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Residual Mixture of Experts

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.417183Z

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Observation a4c6f2f0-ca43-4aef-b87f-b9264cf47e25 · outbound

This paper cites Transfer learning from synthetic to real lidar point cloud for semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Transfer learning from synthetic to real lidar point cloud for semantic segmentation

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.308141Z

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Observation 9719ad43-dada-40f0-ba29-a53e0bc592bd · outbound

This paper cites Unsupervised point cloud rep- resentation learning with deep neural networks: A survey.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Unsupervised point cloud rep- resentation learning with deep neural networks: A survey

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.290566Z

Source-reported events for the cited work

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Observation 9197f8b9-6e32-4615-a219-dae3af22ea9d · outbound

This paper cites 3d semantic segmenta- tion in the wild: Learning generalized models for adverse- condition point clouds.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes 3d semantic segmenta- tion in the wild: Learning generalized models for adverse- condition point clouds

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.272442Z

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Observation e9f34409-c36f-4120-b5ab-ec186f2160ab · outbound

This paper cites Pointcontrast: Unsupervised pre- training for 3d point cloud understanding.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.253572Z

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Observation a8af9759-e174-442f-b79a-353909904248 · outbound

This paper cites Squeeze- segv3: Spatially-adaptive convolution for efficient point- cloud segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Squeeze- segv3: Spatially-adaptive convolution for efficient point- cloud segmentation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.235551Z

Source-reported events for the cited work

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Observation 2019adb4-2c54-42e1-a05b-a7dc78f5a48f · outbound

This paper cites Rpvnet: A deep and efficient range-point- voxel fusion network for lidar point cloud segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Rpvnet: A deep and efficient range-point- voxel fusion network for lidar point cloud segmentation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.217702Z

Source-reported events for the cited work

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Observation ea13855a-1d2a-40d4-8402-db0ec071af47 · outbound

This paper cites 4d contrastive superflows are dense 3d representation learners.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes 4d contrastive superflows are dense 3d representation learners

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.200392Z

Source-reported events for the cited work

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Observation fd6dc428-b4b1-4183-8687-6471774c96bf · outbound

This paper cites Frnet: Frustum-range networks for scalable lidar segmen- tation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Frnet: Frustum-range networks for scalable lidar segmen- tation

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.182054Z

Source-reported events for the cited work

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Observation 9a1170a2-d093-40fd-962e-d3974cd8d4fa · outbound

This paper cites Pointasnl: Robust point clouds process- ing using nonlocal neural networks with adaptive sampling.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Pointasnl: Robust point clouds process- ing using nonlocal neural networks with adaptive sampling

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.164113Z

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Observation c8a3b666-829f-42a6-8c1a-87f7a5910a1e · outbound

This paper cites Second: Sparsely embedded convolutional detection.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Second: Sparsely embedded convolutional detection

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.472107Z

Source-reported events for the cited work

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Observation 8201d6c6-8365-47a6-b743-c5622452af40 · outbound

This paper cites Center-based 3d object detection and tracking.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Center-based 3d object detection and tracking

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.136633Z

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Observation 9101fd5c-ebaa-48c1-a224-cbaeebfc65fb · outbound

This paper cites Self-supervised learning for point cloud data: A survey.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Self-supervised learning for point cloud data: A survey

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.120287Z

Source-reported events for the cited work

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Observation b7b4f07b-60eb-4eb2-9edf-17b9527bbddb · outbound

This paper cites A simple framework for open-vocabulary segmentation and detection.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes A simple framework for open-vocabulary segmentation and detection

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.100523Z

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Observation f58b4042-2363-40c3-ad4d-669c0a1065e4 · outbound

This paper cites Hvdistill: Transferring knowledge from images to point clouds via unsupervised hybrid-view distillation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Hvdistill: Transferring knowledge from images to point clouds via unsupervised hybrid-view distillation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.083595Z

Source-reported events for the cited work

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Observation 5e54d10a-b871-4c9f-843a-2c01ef77d461 · outbound

This paper cites Polarnet: An improved grid representation for online lidar point clouds semantic segmentation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Polarnet: An improved grid representation for online lidar point clouds semantic segmentation

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.065959Z

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Observation 89cede9d-015e-4238-a4d2-3e861d0cd0bc · outbound

This paper cites Self-supervised pretraining of 3d features on any point-cloud.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Self-supervised pretraining of 3d features on any point-cloud

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.047285Z

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Observation 13180f6a-e793-4f31-8592-a4cf302ffa6d · outbound

This paper cites Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.515142Z

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Observation 9671fc77-64f4-47fd-8b57-f012e5c6d43c · outbound

This paper cites Panoptic- polarnet: Proposal-free lidar point cloud panoptic segmen- 26 tation.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Panoptic- polarnet: Proposal-free lidar point cloud panoptic segmen- 26 tation

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.028929Z

Source-reported events for the cited work

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Observation ab89e423-b901-46de-a2f4-cdf82a558343 · outbound

This paper cites LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:05.525373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a9d075-b70c-447f-ad44-9936c2dd2cae · outbound

This paper cites Cylindrical and asymmetrical 3d convolution networks for lidar segmenta- tion.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Cylindrical and asymmetrical 3d convolution networks for lidar segmenta- tion

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:06.009887Z

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Observation 38894f25-21c6-489d-8453-df523b808c36 · outbound

This paper cites Generalized decoding for pixel, image, and language.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Generalized decoding for pixel, image, and language

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:05.989838Z

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Observation 7d1f931c-c178-4ac0-ac72-1fe974bfc73c · outbound

This paper cites Segment everything everywhere all at once.

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes Segment everything everywhere all at once

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:05.972143Z

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Pith citing papers

Observation f5e7ae43-68d6-4710-8f2a-2192f2072366 · inbound

EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media cites this paper.

EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:06.676272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72357686-fe9d-482c-a614-4c8e5f09b4b6 · inbound

Zero-Shot 3D Visual Grounding from Vision-Language Models cites this paper.

Zero-Shot 3D Visual Grounding from Vision-Language Models LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:17.070727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcf1c4c8-753b-473f-86b6-7d36f45fc4e4 · inbound

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers cites this paper.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:26.327329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c4f6ec7-ed0a-4b8d-8ff3-566312cbb660 · inbound

Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation cites this paper.

Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:40:26.991409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ee547b5-7764-4ba3-ab4a-fecdafb891e9 · inbound

OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization cites this paper.

OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:39:03.886167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.