Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving | Paper Citation Record

Observation e72e2a1e-c9e3-4114-8066-9b2e0fb63bd4 · outbound

This paper cites Qwen Technical Report.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Qwen Technical Report

Reference 1

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Observation d66fcc00-127b-4914-8c64-4c9edd6da90a · outbound

This paper cites Transfusion: Ro- bust lidar-camera fusion for 3d object detection with trans- formers.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Transfusion: Ro- bust lidar-camera fusion for 3d object detection with trans- formers

Reference 2

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Observation 63b4f935-7f43-4d97-a21d-8636b7f5ac9e · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving nuscenes: A multi- modal dataset for autonomous driving

Reference 3

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Observation 4d0d5267-deb9-42d4-b02b-d7054dc782d0 · outbound

This paper cites Nuplan: A closed-loop ml-based plan- ning benchmark for autonomous vehicles, 2022.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Nuplan: A closed-loop ml-based plan- ning benchmark for autonomous vehicles, 2022

Reference 4

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Observation 07e58b06-8d32-41f8-8cd1-f897bee6c9ce · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation c72d8efa-0a94-4c10-84fd-9b4d62fd71a8 · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving A simple framework for contrastive learning of visual representations

Reference 6

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Observation f7d1ff38-0f05-4940-9f3f-e48aa94a35b0 · outbound

This paper cites Multi-view 3d object detection network for autonomous driving.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Multi-view 3d object detection network for autonomous driving

Reference 7

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Observation 607f9cf9-42a5-46a3-a452-eb8cb80a8206 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Improved Baselines with Momentum Contrastive Learning

Reference 8

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Observation b6a1d6a7-bf0b-4690-8610-700ad7391b4e · outbound

This paper cites BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection

Reference 9

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Observation d2ed17b4-7f48-4004-9557-5f5304daa1e9 · outbound

This paper cites Back-tracing representative points for voting- based 3d object detection in point clouds.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Back-tracing representative points for voting- based 3d object detection in point clouds

Reference 10

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Observation d0c19000-001c-4c47-89e8-26aa6b069643 · outbound

This paper cites Lyft 3d object detection for autonomous vehicles.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Lyft 3d object detection for autonomous vehicles

Reference 11

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Observation 5218d50f-fe7a-4ec5-a78c-08f5947374c8 · outbound

This paper cites Scaling vision transformers to 22 billion pa- rameters.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Scaling vision transformers to 22 billion pa- rameters

Reference 12

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Observation 92757011-8770-453a-9af1-5733e097c343 · outbound

This paper cites V oxel r-cnn: To- wards high performance voxel-based 3d object detec- tion.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving V oxel r-cnn: To- wards high performance voxel-based 3d object detec- tion

Reference 13

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Observation b0fd37f0-c2e6-45b8-95b2-72c1116ad499 · outbound

This paper cites Embracing single stride 3d object detector with sparse trans- former.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Embracing single stride 3d object detector with sparse trans- former

Reference 14

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Observation a680525e-cc87-4285-b76a-bb8b4c30ed88 · outbound

This paper cites Sparse dense fusion for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Sparse dense fusion for 3d object detection

Reference 15

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Observation 98a104f9-f67f-4936-ab26-be96a0c0fb96 · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Momentum contrast for unsupervised visual rep- resentation learning

Reference 16

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Observation 66d0ca62-ca8f-416a-bac3-2370bec36515 · outbound

This paper cites Masked autoencoders are scalable vision learners, 2021.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Masked autoencoders are scalable vision learners, 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.276923Z

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Observation 089bfaa8-7625-40f0-8fe2-7b840912a737 · outbound

This paper cites Data-efficient image recognition with con- trastive predictive coding.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Data-efficient image recognition with con- trastive predictive coding

Reference 18

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Observation 73d38042-0a3f-4c3f-a310-8657655e4257 · outbound

This paper cites Masked autoencoder for self-supervised pre-training on lidar point clouds.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Masked autoencoder for self-supervised pre-training on lidar point clouds

Reference 19

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Observation 6f9926f3-f595-4ed4-a807-149d362b0cb7 · outbound

This paper cites BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View

Reference 20

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Observation d9eaeec5-7be3-472c-9334-e772ad785941 · outbound

This paper cites Ep- net: Enhancing point features with image semantics for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Ep- net: Enhancing point features with image semantics for 3d object detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.229691Z

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Observation 7fd0c8c0-7fc3-4343-8305-72f792b8985a · outbound

This paper cites Tri-perspective view for vision- based 3d semantic occupancy prediction.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Tri-perspective view for vision- based 3d semantic occupancy prediction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.210127Z

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Observation 97b86348-507e-4bb5-80a9-61c8353fcab0 · outbound

This paper cites Learning semantic segmentation from multiple datasets with label shifts.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Learning semantic segmentation from multiple datasets with label shifts

Reference 23

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Observation 71585212-1172-44f1-acdd-260fce955433 · outbound

This paper cites OccMamba: Semantic Occupancy Prediction with State Space Models.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving OccMamba: Semantic Occupancy Prediction with State Space Models

Reference 24

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Observation ec34b3f8-a8a7-49d0-bc9c-a944839062a6 · outbound

This paper cites Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.172644Z

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Observation 65131e67-1e96-450d-8aa8-8904315e788b · outbound

This paper cites Simipu: Simple 2d image and 3d point cloud un- supervised pre-training for spatial-aware visual representa- tions.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Simipu: Simple 2d image and 3d point cloud un- supervised pre-training for spatial-aware visual representa- tions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.152448Z

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Observation b4e4d9f2-a8d4-4bf5-b411-d50e69850c26 · outbound

This paper cites Bev- former: Learning bird’s-eye-view representation from multi- camera images via spatiotemporal transformers.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Bev- former: Learning bird’s-eye-view representation from multi- camera images via spatiotemporal transformers

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.136244Z

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Observation 0a9ca2cf-a20f-4435-9e2d-2699ae2da981 · outbound

This paper cites Bevfusion: A simple and robust lidar-camera fu- sion framework.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Bevfusion: A simple and robust lidar-camera fu- sion framework

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.119937Z

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Observation 28fca571-aead-4149-bba9-21e4922bc6ec · outbound

This paper cites Geomim: Towards better 3d knowledge transfer via masked image modeling for multi-view 3d un- derstanding.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Geomim: Towards better 3d knowledge transfer via masked image modeling for multi-view 3d un- derstanding

Reference 29

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Observation 0fc43de0-54a0-4fc7-b8ba-4f6b99ac018a · outbound

This paper cites P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

Reference 30

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Observation fe8faa54-45f3-4beb-81ee-2ef73f05460d · outbound

This paper cites Petr: Position embedding transformation for multi-view 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Petr: Position embedding transformation for multi-view 3d object detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.083322Z

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Observation 46289bd7-e323-4e71-a664-46562fdec579 · outbound

This paper cites Swin trans- former: Hierarchical vision transformer using shifted win- dows, 2021.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Swin trans- former: Hierarchical vision transformer using shifted win- dows, 2021

Reference 32

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Observation 82dc1b25-46a9-4e80-908c-86673467a312 · outbound

This paper cites Bevfusion: Multi- task multi-sensor fusion with unified bird’s-eye view repre- sentation.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Bevfusion: Multi- task multi-sensor fusion with unified bird’s-eye view repre- sentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.055953Z

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Observation de6c53ea-46f2-45c9-b663-ca8702143c84 · outbound

This paper cites One Million Scenes for Autonomous Driving: ONCE Dataset.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving One Million Scenes for Autonomous Driving: ONCE Dataset

Reference 34

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Observation 8e771814-ec7a-49f8-a268-5fe4ee774820 · outbound

This paper cites V oxel transformer for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving V oxel transformer for 3d object detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.037906Z

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Observation a2acd7c4-c104-4838-a26a-348a22eea51c · outbound

This paper cites Multi-camera unified pre-training via 3d scene recon- struction.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Multi-camera unified pre-training via 3d scene recon- struction

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.020710Z

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Observation 382dff7f-c24b-4d35-bf0f-3a5c349e3da2 · outbound

This paper cites Uniscene: Multi-camera unified pre-training via 3d scene reconstruction for autonomous driving, 2024.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Uniscene: Multi-camera unified pre-training via 3d scene reconstruction for autonomous driving, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:06.003658Z

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Observation b3a4e140-91d8-41d1-8c22-e8b7066e602e · outbound

This paper cites Self-supervised learning of pretext-invariant representations.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Self-supervised learning of pretext-invariant representations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.985516Z

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Observation a17bb197-64d9-4fa7-92fd-d4802ecd0248 · outbound

This paper cites Masked autoencoders for point cloud self-supervised learning.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Masked autoencoders for point cloud self-supervised learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.969142Z

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Observation 8d4b43a7-29c5-46a6-8169-1568233ce3da · outbound

This paper cites Simpletrack: Understanding and rethinking 3d multi-object tracking.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Simpletrack: Understanding and rethinking 3d multi-object tracking

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.952969Z

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Observation b089837e-43fb-49fa-a2a7-b3171c396649 · outbound

This paper cites Standing between past and future: Spatio-temporal modeling for multi-camera 3d multi- object tracking.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Standing between past and future: Spatio-temporal modeling for multi-camera 3d multi- object tracking

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.935375Z

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Observation a58a8716-1fab-4a4d-90eb-31829caef806 · outbound

This paper cites What Do Self-Supervised Vision Transformers Learn?.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving What Do Self-Supervised Vision Transformers Learn?

Reference 42

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Observation 22d1af9b-6df6-4419-a516-9cb00d7e5871 · outbound

This paper cites Lift, splat, shoot: En- coding images from arbitrary camera rigs by implicitly un- projecting to 3d.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Lift, splat, shoot: En- coding images from arbitrary camera rigs by implicitly un- projecting to 3d

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.918876Z

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Observation bd90abcb-f341-43ca-8e03-8a06373428f1 · outbound

This paper cites Deep hough voting for 3d object detection in point clouds.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Deep hough voting for 3d object detection in point clouds

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.901850Z

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Observation 12d9a611-b081-4544-b5a0-2dbb6954f258 · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Learning transferable visual models from natural language supervision, 2021

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.883422Z

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Observation 067ba32f-7414-46fa-a9d4-3e7961cf9b8d · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Image-to-lidar self-supervised distillation for autonomous driving data,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.867407Z

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Observation 441789eb-f4c8-4084-9466-02b10e4434ce · outbound

This paper cites Pointr- cnn: 3d object proposal generation and detection from point cloud.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Pointr- cnn: 3d object proposal generation and detection from point cloud

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.793465Z

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Observation bc8433c6-2d9a-466a-901e-e83bb8d9437d · outbound

This paper cites Pv-rcnn: Point-voxel feature set abstraction for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Pv-rcnn: Point-voxel feature set abstraction for 3d object detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.840845Z

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Observation 711ba46e-d296-4241-895d-c68d1081852f · outbound

This paper cites From points to parts: 3d object detec- tion from point cloud with part-aware and part-aggregation network.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving From points to parts: 3d object detec- tion from point cloud with part-aware and part-aggregation network

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.824006Z

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Observation 759d9ceb-e6c0-44f1-a6e2-4e4c2f273872 · outbound

This paper cites Mvx- net: Multimodal voxelnet for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Mvx- net: Multimodal voxelnet for 3d object detection

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.808012Z

Source-reported events for the cited work

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Observation e0bd6fcd-9720-42f1-96b4-cec0a5d0e4d8 · outbound

This paper cites CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.812438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c774d46b-34e7-42cb-a9b4-ac89e00497aa · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Scalability in perception for autonomous driving: Waymo open dataset

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.791952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 53296ce4-7430-4028-99b0-6954e4a48747 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving LLaMA: Open and Efficient Foundation Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.821696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9013c791-794b-4faa-a182-5b20bf3d9467 · outbound

This paper cites Pointpainting: Sequential fusion for 3d object de- tection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Pointpainting: Sequential fusion for 3d object de- tection

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.826720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e97f82f-3563-41fd-b699-934a97a97fec · outbound

This paper cites Pointaugmenting: Cross-modal augmentation for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Pointaugmenting: Cross-modal augmentation for 3d object detection

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.766198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6a35e5f2-8da3-4e26-b80b-d5ba1351f7cb · outbound

This paper cites Rbgnet: Ray-based grouping for 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Rbgnet: Ray-based grouping for 3d object detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.750442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f2524166-d622-4b6a-af02-e075a62f82fa · outbound

This paper cites Dsvt: Dy- namic sparse voxel transformer with rotated sets, 2023.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Dsvt: Dy- namic sparse voxel transformer with rotated sets, 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.735440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d4dbffb-8fbe-4a31-a2d8-ad18531ee729 · outbound

This paper cites Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view repre- sentation.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view repre- sentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.720762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 007d9845-ff13-4cfa-b028-321adec6f841 · outbound

This paper cites Cross-dataset collaborative learning for seman- tic segmentation in autonomous driving.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Cross-dataset collaborative learning for seman- tic segmentation in autonomous driving

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.704281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16d8a9ce-5bd2-4f14-816d-73aa3931097d · outbound

This paper cites Mv- contrast: Unsupervised pretraining for multi-view 3d object recognition.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Mv- contrast: Unsupervised pretraining for multi-view 3d object recognition

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.687952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 795e196f-bedf-441e-8bf3-6abc2b608bdf · outbound

This paper cites Tracking everything everywhere all at once.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Tracking everything everywhere all at once

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.669575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6942a76-e74d-4def-a3ed-e016705bbd7d · outbound

This paper cites Fcos3d: Fully convolutional one-stage monocular 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Fcos3d: Fully convolutional one-stage monocular 3d object detection

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.651697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 815aa0ba-092b-4f3a-abc7-051cfe9ceefa · outbound

This paper cites Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.630559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e1e4659-1248-4419-b092-7480dd02e2fb · outbound

This paper cites Detr3d: 3d object detection from multi-view images via 3d-to-2d queries.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Detr3d: 3d object detection from multi-view images via 3d-to-2d queries

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.609928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93aaf10b-9847-40ba-ad20-e5dcc37610dc · outbound

This paper cites Masked feature predic- tion for self-supervised visual pre-training.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Masked feature predic- tion for self-supervised visual pre-training

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.589009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c53a3cda-4791-4f94-8b56-9554a4035a2a · outbound

This paper cites Virtual sparse convolution for multimodal 3d ob- ject detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Virtual sparse convolution for multimodal 3d ob- ject detection

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.571702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7bd373c8-3107-4676-a4a4-f7d5f3890473 · outbound

This paper cites Towards large-scale 3d representation learning with multi-dataset point prompt training.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Towards large-scale 3d representation learning with multi-dataset point prompt training

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.554412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation faa3470d-ce8e-44c9-ae3c-ae33d20e704e · outbound

This paper cites M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.895032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c8a4479-cd35-4eac-b096-376af9139f9f · outbound

This paper cites Pointcontrast: Unsupervised pre- 11 training for 3d point cloud understanding.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Pointcontrast: Unsupervised pre- 11 training for 3d point cloud understanding

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.537360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d3b27e7-539a-42f5-af28-bcbac3e648d8 · outbound

This paper cites Qi, Leonidas J.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Qi, Leonidas J

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.520405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9568c564-2863-49ec-9f9c-f74133bfdaef · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Simmim: A simple framework for masked image modeling

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.503265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0158ed4e-f3c0-4d85-bd18-25eb96396fa4 · outbound

This paper cites Cross modal trans- former: Towards fast and robust 3d object detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Cross modal trans- former: Towards fast and robust 3d object detection

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.484923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8a26a85-bb4c-4141-9708-ec40c172cd99 · outbound

This paper cites SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.918936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e66dd811-29ac-4201-9bd2-437a68ce9aae · outbound

This paper cites Second: Sparsely embed- ded convolutional detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Second: Sparsely embed- ded convolutional detection

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.469494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5cddb47-7c53-4fce-89f6-2b76cab1f634 · outbound

This paper cites Boosting 3D Object Detection via Object-Focused Image Fusion.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Boosting 3D Object Detection via Object-Focused Image Fusion

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.929624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bae362b-5d29-441d-972b-3ce993682baf · outbound

This paper cites Gd-mae: Gen- erative decoder for mae pre-training on lidar point clouds,.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Gd-mae: Gen- erative decoder for mae pre-training on lidar point clouds,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.453699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13aefb75-e5ab-4689-9634-7608ad071a3a · outbound

This paper cites Pred: pre-training via semantic rendering on lidar point clouds.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Pred: pre-training via semantic rendering on lidar point clouds

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.436633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27384206-382a-4dfe-9272-eb97fc8ee122 · outbound

This paper cites 3dssd: Point-based 3d single stage object detector.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving 3dssd: Point-based 3d single stage object detector

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.419325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74519015-6f4d-48fc-b2c5-18df8ee25ba6 · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Center- based 3d object detection and tracking

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.403184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f39a6e44-707e-43a2-89ef-9604fd80d027 · outbound

This paper cites 3d-cvf: Generating joint camera and lidar fea- tures using cross-view spatial feature fusion for 3d ob- ject detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving 3d-cvf: Generating joint camera and lidar fea- tures using cross-view spatial feature fusion for 3d ob- ject detection

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.386205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9b68d7a-11af-47ae-8541-f2bd9cbdf0d8 · outbound

This paper cites SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.963199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 661e0502-73ce-4c45-a5e8-8c80045eeb71 · outbound

This paper cites Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.368023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d82f25d1-9b79-4b79-a14d-21f7a3d85aaf · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Hvdistill: Transferring knowl- edge from images to point clouds via unsupervised hybrid- view distillation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.351182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 499a02cd-1d0b-4ce9-85f6-5bf4882c3fe0 · outbound

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

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Self-supervised pretraining of 3d features on any point-cloud

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.335464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ff301f7-a203-4bae-a331-d710e3274e89 · outbound

This paper cites Unidistill: A universal cross-modality knowl- edge distillation framework for 3d object detection in bird’s- eye view, 2023.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Unidistill: A universal cross-modality knowl- edge distillation framework for 3d object detection in bird’s- eye view, 2023

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.319944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 931d9d47-bb01-4354-938e-3d9b8cae00f7 · outbound

This paper cites Sim- ple multi-dataset detection.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Sim- ple multi-dataset detection

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.302684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 621502f4-3981-4bbe-bba0-f7bbd8d943bf · outbound

This paper cites Cylindrical and asymmetrical 3d convolution networks for lidar seg- mentation.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving Cylindrical and asymmetrical 3d convolution networks for lidar seg- mentation

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T12:29:04.992486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 938485b7-09dd-447a-851d-1b49ebbb6fdf · outbound

This paper cites NuScenes.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving NuScenes

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:29:05.273987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Reference resolution

Outbound references

Pith citing papers