LeAP: Consistent multi-domain 3D labeling using Foundation Models | Paper Citation Record

Observation 60a64aa9-3d4c-4824-b1a9-1e2cad500784 · outbound

This paper cites nuScenes: A Multimodal Dataset for Autonomous Driving,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models nuScenes: A Multimodal Dataset for Autonomous Driving,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.411499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51cc88cf-d42f-407a-8f89-993b4ad27fb6 · outbound

This paper cites Scalability in Perception for Autonomous Driving: Waymo Open Dataset.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.417130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a99eea1c-ac31-4d04-8018-9d11741ccd4f · outbound

This paper cites Are we ready for autonomous driving? The KITTI vision benchmark suite,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Are we ready for autonomous driving? The KITTI vision benchmark suite,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.422654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aa10233-3666-47ac-b127-500adbf902f0 · outbound

This paper cites SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.427642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 159987c3-71ed-4e2d-a829-af71520b5972 · outbound

This paper cites KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D.

LeAP: Consistent multi-domain 3D labeling using Foundation Models KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.433102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52bb1065-449b-4e6c-8520-b750a2b2eb46 · outbound

This paper cites PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.438601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4611a4a9-561a-4b01-8887-e23048826bc8 · outbound

This paper cites Offline Tracking with Object Permanence.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Offline Tracking with Object Permanence

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:18.055670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f98d050d-668c-46c7-8445-d73db37478b6 · outbound

This paper cites Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.450428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81544536-46de-4f67-8233-d695eea66009 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Learning Transferable Visual Models From Natural Language Supervision

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.456179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28886f5c-4706-4bc4-a644-d08f4afb1950 · outbound

This paper cites Segment Anything.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Segment Anything

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.461657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e77f8d4d-5adf-4bce-bfe7-aca97bd38409 · outbound

This paper cites Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.467101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbf95e82-94d3-499f-a48e-ea2308f65409 · outbound

This paper cites PointCLIP: Point Cloud Understanding by CLIP,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointCLIP: Point Cloud Understanding by CLIP,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:17.961925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a576ed46-42b0-4ca4-ab23-80a1f24bf1d6 · outbound

This paper cites OpenScene: 3D Scene Understanding with Open Vocabularies.

LeAP: Consistent multi-domain 3D labeling using Foundation Models OpenScene: 3D Scene Understanding with Open Vocabularies

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.476143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7681a34f-6c1a-436c-975c-1de8c891c6b4 · outbound

This paper cites CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP.

LeAP: Consistent multi-domain 3D labeling using Foundation Models CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.851071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c6b7f1df-45d7-4976-a757-718a0c98a69b · outbound

This paper cites OVO: Open-Vocabulary Occupancy.

LeAP: Consistent multi-domain 3D labeling using Foundation Models OVO: Open-Vocabulary Occupancy

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.484766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a75c7ca6-4b79-43a6-bc7c-eba1913346a8 · outbound

This paper cites POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images.

LeAP: Consistent multi-domain 3D labeling using Foundation Models POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.489082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7dae428-4841-4f95-901c-22ef7625fffb · outbound

This paper cites LidarCLIP or: How I Learned to Talk to Point Clouds,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models LidarCLIP or: How I Learned to Talk to Point Clouds,

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:17.791624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 221cb6fc-8f8b-4fe7-ac7c-6252c5a51cbd · outbound

This paper cites Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.499494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f987f46-0ebb-47f4-b5d0-00d90acc9530 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.504820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46148819-ff77-42d9-84d0-c64bbc46d9b0 · outbound

This paper cites V oxNet: A 3D Convolutional Neural Network for real-time object recognition,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models V oxNet: A 3D Convolutional Neural Network for real-time object recognition,

Reference 20

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:17.635304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6904442e-efe0-4bab-8b54-0b6e084d563c · outbound

This paper cites 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks.

LeAP: Consistent multi-domain 3D labeling using Foundation Models 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.515184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5ba1b1c-628e-4a50-9be3-b0fb719e3ee3 · outbound

This paper cites Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.515570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5d14215-619f-485d-a96c-47b564b1a93e · outbound

This paper cites Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.525833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23e074ac-95cd-4d3a-9a84-b848957a9f07 · outbound

This paper cites SV ASeg: Sparse V oxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SV ASeg: Sparse V oxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:21:18.331088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e526c4d-38b0-4f73-bf3d-b0dfbc98a989 · outbound

This paper cites Spherical Transformer for LiDAR-based 3D Recognition.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Spherical Transformer for LiDAR-based 3D Recognition

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.472326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a8b42bc-e8a0-485e-ade9-e81b3c550b48 · outbound

This paper cites OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction.

LeAP: Consistent multi-domain 3D labeling using Foundation Models OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.541222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eaa1d1e-233a-4192-b283-8521168e930c · outbound

This paper cites MonoScene: Monocular 3D Semantic Scene Completion.

LeAP: Consistent multi-domain 3D labeling using Foundation Models MonoScene: Monocular 3D Semantic Scene Completion

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.546503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eccbcbd-ad38-4330-974d-97cc93a9bd70 · outbound

This paper cites SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.551564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a39c34ee-884d-4b68-a7e5-14812304f548 · outbound

This paper cites RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation,

Reference 29

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:17.397943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 48296489-b7da-4b17-8f26-8adc150991c3 · outbound

This paper cites SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.561750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ba31ed7-6c01-48df-a928-109660d592dc · outbound

This paper cites PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.301679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf06d035-b172-45e9-a85b-564de90959cd · outbound

This paper cites Rethinking Range View Representation for LiDAR Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Rethinking Range View Representation for LiDAR Segmentation

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.278613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c8a075a0-028b-4ff2-944d-f86ac1cf6797 · outbound

This paper cites LMSCNet: Lightweight Multiscale 3D Semantic Completion.

LeAP: Consistent multi-domain 3D labeling using Foundation Models LMSCNet: Lightweight Multiscale 3D Semantic Completion

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.255397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b2c28e7-b3a7-431d-803d-e6c51af4f211 · outbound

This paper cites S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds.

LeAP: Consistent multi-domain 3D labeling using Foundation Models S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.231232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 56a975bf-9ad9-43c8-9d9b-7a885a9e2463 · outbound

This paper cites PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.587482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edf6a45f-810d-4259-8505-f53ad4fa2fcf · outbound

This paper cites Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.591816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97682cf1-519c-4ca2-a18f-6222af35490f · outbound

This paper cites PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.596109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e739dea-f2fc-42b4-a54c-87b94262352a · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.600262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f0c423a-4e7c-4f16-87b7-9f8b574b5639 · outbound

This paper cites KPConv: Flexible and Deformable Convolution for Point Clouds.

LeAP: Consistent multi-domain 3D labeling using Foundation Models KPConv: Flexible and Deformable Convolution for Point Clouds

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.604689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 885b5f1f-95c1-427b-8322-78fe1e78c0bf · outbound

This paper cites RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds.

LeAP: Consistent multi-domain 3D labeling using Foundation Models RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.609814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f9adfae-a589-4684-b568-89c03ecadf51 · outbound

This paper cites Point Transformer V2: Grouped Vector Attention and Partition-based Pooling.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.614691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e5abf3-37ed-4d6e-8f26-4ff2f9c88c22 · outbound

This paper cites (AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network.

LeAP: Consistent multi-domain 3D labeling using Foundation Models (AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.620176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2494b17f-c9a0-4c3f-91cd-0176ac1bad41 · outbound

This paper cites RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:17.073146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e917c460-af91-491d-b691-e46f47adf7fc · outbound

This paper cites LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception.

LeAP: Consistent multi-domain 3D labeling using Foundation Models LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.630087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8108755b-746b-4843-abce-7d7c0f17be2e · outbound

This paper cites LiDAR-Camera Continuous Fusion in V oxelized Grid for Semantic Scene Completion,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models LiDAR-Camera Continuous Fusion in V oxelized Grid for Semantic Scene Completion,

Reference 45

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:17.033077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 66c24aba-a8fe-4c12-aa1c-48c35bb6faeb · outbound

This paper cites GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds.

LeAP: Consistent multi-domain 3D labeling using Foundation Models GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.943187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c585b93-7ed4-4bde-b961-fed023ed10b2 · outbound

This paper cites U3DS$^3$: Unsupervised 3D Semantic Scene Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models U3DS$^3$: Unsupervised 3D Semantic Scene Segmentation

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.918821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 675af4aa-bab6-454c-8f8d-151138d5df21 · outbound

This paper cites PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.650253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4276787-2d4c-4120-9d1f-5e5380d3e54a · outbound

This paper cites Self-Supervised Pretraining of 3D Features on any Point-Cloud,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Self-Supervised Pretraining of 3D Features on any Point-Cloud,

Reference 49

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:16.877858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 215d71d4-f583-43ab-a52d-1e715b100b98 · outbound

This paper cites SegContrast: 3D Point Cloud Feature Representation Learning Through Self-Supervised Segment Discrimination,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SegContrast: 3D Point Cloud Feature Representation Learning Through Self-Supervised Segment Discrimination,

Reference 50

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:16.782460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 84032960-1f51-4456-9a2e-6b73060059a0 · outbound

This paper cites S4C: Self-Supervised Semantic Scene Completion with Neural Fields.

LeAP: Consistent multi-domain 3D labeling using Foundation Models S4C: Self-Supervised Semantic Scene Completion with Neural Fields

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.689032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a79acaca-85fb-434a-b618-774449ee1220 · outbound

This paper cites OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments.

LeAP: Consistent multi-domain 3D labeling using Foundation Models OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.670429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26792d01-dc17-4591-b56f-b0fe3dbda0bb · outbound

This paper cites Learning 3D Semantic Segmentation with only 2D Image Supervision,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Learning 3D Semantic Segmentation with only 2D Image Supervision,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.675496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0546ccd3-f00c-4a2a-bad5-26a07583cb7d · outbound

This paper cites Real-time multi- modal semantic fusion on unmanned aerial vehicles with label propagation for cross-domain adaptation,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Real-time multi- modal semantic fusion on unmanned aerial vehicles with label propagation for cross-domain adaptation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:21:18.314768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f791c5ea-ef06-4613-bb69-9366b2d29af8 · outbound

This paper cites Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data,

Reference 55

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:16.563769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 507e3f1c-4092-4718-aa08-99e46b515a3b · outbound

This paper cites Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.484631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cdccce3-e9bb-4a17-98d4-4d75906cf8cf · outbound

This paper cites Segment Any Point Cloud Sequences by Distilling Vision Foundation Models.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.695219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f763e50b-2c76-4457-94bd-2ec63ee4bda3 · outbound

This paper cites PointPainting: Sequential Fusion for 3D Object Detection.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointPainting: Sequential Fusion for 3D Object Detection

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.701262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 251003e1-ae1a-4683-a882-4fa3d201b623 · outbound

This paper cites 360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models 360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.424559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05699700-528c-4e82-81a2-89c34cee9987 · outbound

This paper cites Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.709740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf0814b6-8520-48ff-a453-a2a13b9d2804 · outbound

This paper cites SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.714057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a080da4-5ac0-499d-9648-5e296ee2b261 · outbound

This paper cites Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.364730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38fec1ee-cdd2-4268-a8ff-cd5c879dbac3 · outbound

This paper cites OpenAnnotate3D: Open-V ocabulary Auto-Labeling System for Multi-modal 3D Data,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models OpenAnnotate3D: Open-V ocabulary Auto-Labeling System for Multi-modal 3D Data,

Reference 63

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:16.343812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e41d27de-8e33-446f-b3ea-669286cc56f8 · outbound

This paper cites OpenAnnotate2: Multi-Modal Auto-Annotating for Autonomous Driving,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models OpenAnnotate2: Multi-Modal Auto-Annotating for Autonomous Driving,

Reference 64

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:16.261791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31463c03-ffa5-4640-8841-f27d1a249d8b · outbound

This paper cites SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks.

LeAP: Consistent multi-domain 3D labeling using Foundation Models SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:16.154187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95788f76-b013-4034-9d11-a0aeafc1ca2f · outbound

This paper cites Real-time 3D reconstruction at scale using voxel hashing,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Real-time 3D reconstruction at scale using voxel hashing,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.738334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 644442ec-c323-4219-a3e6-66be3309ad05 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Distilling the Knowledge in a Neural Network

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.743145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b402094-2189-468a-8c83-60c06a44a181 · outbound

This paper cites Learning to Detect Mobile Objects from LiDAR Scans Without Labels,.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Learning to Detect Mobile Objects from LiDAR Scans Without Labels,

Reference 68

Resolution: verified exact; raw_fallback, observed 2026-08-09T00:21:16.035204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 196d6369-a73e-4b14-b696-01b62c2cbc58 · outbound

This paper cites Label-Efficient 3D Object Detection For Road-Side Units.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Label-Efficient 3D Object Detection For Road-Side Units

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:15.885669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc770e94-9158-4cee-a092-37fd460933f2 · outbound

This paper cites Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:15.855459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27f00ced-6d44-423c-bbb2-649062fcb5e7 · outbound

This paper cites AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles.

LeAP: Consistent multi-domain 3D labeling using Foundation Models AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.762833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d51a3d75-8e88-4958-9271-4265befddcd0 · outbound

This paper cites Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:21:15.814678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers