Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities | Paper Citation Record

Observation f9257e52-cd33-479b-abe0-31b69599653d · outbound

This paper cites 3d object detection for autonomous driving: A survey,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities 3d object detection for autonomous driving: A survey,

Reference 1

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Observation c764dff8-139f-46ed-bdb8-23c5de0b39fb · outbound

This paper cites Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and out- looks,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and out- looks,

Reference 2

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Observation 5f403c37-4c36-41ed-8fb0-76bce0f8ac70 · outbound

This paper cites A review of deep learning-based visual multi-object tracking algorithms for autonomous driving,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities A review of deep learning-based visual multi-object tracking algorithms for autonomous driving,

Reference 3

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Observation d65b8950-a850-4da0-acd1-0ccb76d5e3ae · outbound

This paper cites Towards long-tailed 3d detection,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Towards long-tailed 3d detection,

Reference 4

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Observation e465f32f-7cec-4df0-ad99-fe71a4c90dd1 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities On the Opportunities and Risks of Foundation Models

Reference 5

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This paper cites Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities

Reference 6

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Observation 376c9556-8aff-430c-8e30-75316db34504 · outbound

This paper cites Applications of Large Scale Foundation Models for Autonomous Driving.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Applications of Large Scale Foundation Models for Autonomous Driving

Reference 7

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Observation d0e341af-95d9-4c15-8f84-6db07cbdc06b · outbound

This paper cites A Survey for Foundation Models in Autonomous Driving.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities A Survey for Foundation Models in Autonomous Driving

Reference 8

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Observation 1888f426-0ae8-4a6a-a30a-965628e0aa72 · outbound

This paper cites Prospective role of foundation models in advancing autonomous vehicles,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Prospective role of foundation models in advancing autonomous vehicles,

Reference 9

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Observation 044ac690-0940-4526-a418-a6c219682e59 · outbound

This paper cites LLM4Drive: A Survey of Large Language Models for Autonomous Driving.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities LLM4Drive: A Survey of Large Language Models for Autonomous Driving

Reference 10

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Observation d9f5dca6-921b-453c-bc8c-5a1e01fa2b39 · outbound

This paper cites Vision language models in autonomous driving: A survey and outlook,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Vision language models in autonomous driving: A survey and outlook,

Reference 11

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Observation de83e643-f1ad-4c54-9db8-23edaf01ae9f · outbound

This paper cites A simple framework for contrastive learning of vi- sual representations,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities A simple framework for contrastive learning of vi- sual representations,

Reference 12

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Observation 5bad0149-16d2-4d99-a209-330436757d0d · outbound

This paper cites Momentum contrast for unsupervised visual repre- sentation learning,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Momentum contrast for unsupervised visual repre- sentation learning,

Reference 13

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Observation a1f52ee6-a4a5-4f23-a82e-2645a82f60fb · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Improved Baselines with Momentum Contrastive Learning

Reference 14

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Observation 2925c295-deca-4236-ad6e-2b8327b9cd3c · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Masked autoencoders are scalable vision learners,

Reference 15

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Observation d69169fb-6421-4219-bfed-0898ef033262 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Observation b399a9ea-240a-4d5c-a12f-56223cef3f85 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Distilling the Knowledge in a Neural Network

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Observation 7a598d00-ea11-4715-a1d1-607a76e0c35d · outbound

This paper cites Knowledge distillation: A survey,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Knowledge distillation: A survey,

Reference 18

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Observation 75a8cb68-f62b-4daa-a0b1-342804835a49 · outbound

This paper cites Self- training with noisy student improves imagenet classi- fication,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Self- training with noisy student improves imagenet classi- fication,

Reference 19

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Observation 4ba07694-93b0-44ed-9bf7-c19fb1310825 · outbound

This paper cites Bootstrap your own latent-a new approach to self- supervised learning,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Bootstrap your own latent-a new approach to self- supervised learning,

Reference 20

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Observation 29de20c4-8203-44df-8485-5d5e16fd24b2 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Emerging properties in self-supervised vision transformers,

Reference 21

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Observation 997aae34-57c4-4592-92e7-b5ab8cd71ea4 · outbound

This paper cites Structure-from- motion revisited,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Structure-from- motion revisited,

Reference 22

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Observation d9c70e9f-8a34-4c5f-b79f-5ae34ecf8564 · outbound

This paper cites Multi-view stereo: A tutorial,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Multi-view stereo: A tutorial,

Reference 23

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Observation 4546bf8d-89ba-441f-a2e0-d6ccdc165bf5 · outbound

This paper cites Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,

Reference 24

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Observation 96ab3df6-150f-4ccf-a4c3-f3435abbd036 · outbound

This paper cites 3d-r2n2: A unified approach for single and multi- view 3d object reconstruction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities 3d-r2n2: A unified approach for single and multi- view 3d object reconstruction,

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Observation 0e257a96-f1ae-48f4-8d41-bbbc38e21073 · outbound

This paper cites Predicting depth, surface normals and semantic labels with a common multi- scale convolutional architecture,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Predicting depth, surface normals and semantic labels with a common multi- scale convolutional architecture,

Reference 26

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Observation bbb8a972-b932-4b27-96bd-8e63b568c99d · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 27

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Observation 1dfc9792-243d-4bc1-8da5-8ebb0307a6e2 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities 3d gaussian splatting for real-time radiance field rendering

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Observation 7465bef0-9337-46c8-912c-65294b4f72fb · outbound

This paper cites YOLOv3: An Incremental Improvement.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities YOLOv3: An Incremental Improvement

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Observation 8d63dc12-8850-4364-8ce0-a4ae4ccea62f · outbound

This paper cites Deep residual learning for image recognition,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Deep residual learning for image recognition,

Reference 30

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Observation 6bbaf5a6-75be-4b30-b814-8e055348648e · outbound

This paper cites GPT-4 Technical Report.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities GPT-4 Technical Report

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Observation 306ef2ef-2bec-429d-880a-6fdb354c8660 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Bert: Pre-training of deep bidirectional transformers for language understanding,

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Observation 808ca6a7-f1cc-4b29-828d-6a2e237583af · outbound

This paper cites Segment anything,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Segment anything,

Reference 33

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Observation 3b690062-15d8-4053-9c37-476f70e7730b · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities DINOv2: Learning Robust Visual Features without Supervision

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Observation ed7d736d-bf60-42b9-b47b-a6ce60c45e49 · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Learning transferable visual models from natural language supervision,

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Observation 491988f0-11c5-474c-9803-79e69ab57e2d · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for VOLUME 00, 2024 27 Authoret al.: Preparation of Papers for IEEE OPEN JOURNALS open-set object detection,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Grounding dino: Marrying dino with grounded pre-training for VOLUME 00, 2024 27 Authoret al.: Preparation of Papers for IEEE OPEN JOURNALS open-set object detection,

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Observation c087e1f2-c3d8-45da-b1b0-21f0d6b1d44b · outbound

This paper cites Denoising diffusion probabilistic models,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Denoising diffusion probabilistic models,

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Observation 45984016-7677-47d3-96dd-fbc7e6d7a997 · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities High-resolution image synthesis with latent diffusion models,

Reference 38

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Observation 06588573-edd6-4f41-82f2-8f80df7780c8 · outbound

This paper cites Video diffusion models,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Video diffusion models,

Reference 39

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Observation cb6f5e0e-29a8-4af9-88d8-6d20ad38a38b · outbound

This paper cites 3d shape generation and completion through point-voxel diffusion,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities 3d shape generation and completion through point-voxel diffusion,

Reference 40

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Observation 254311b5-9d0d-4818-b96f-c978d422a22f · outbound

This paper cites Diffusion-based signed distance fields for 3d shape generation,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Diffusion-based signed distance fields for 3d shape generation,

Reference 41

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Observation 1c0950a9-d402-41ed-875f-898878a7fcf2 · outbound

This paper cites Language models are few-shot learners,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Language models are few-shot learners,

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Observation a6b6b936-605d-4ed9-a1ac-5a8190f8a493 · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Image-to-lidar self-supervised distilla- tion for autonomous driving data,

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Observation 48a87cf9-5322-4f28-b184-8470b2895fb4 · outbound

This paper cites Segment any point cloud sequences by distilling vision foundation models,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Segment any point cloud sequences by distilling vision foundation models,

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Observation 6b775d6d-9da4-45eb-9ceb-f1a6ea681d32 · outbound

This paper cites Better call sal: Towards learning to segment anything in lidar,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Better call sal: Towards learning to segment anything in lidar,

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Observation 6faf96f5-d14a-42f4-b8c5-6b7743b1559d · outbound

This paper cites Sam4udass: When sam meets unsupervised domain adaptive semantic segmentation in intelligent ve- hicles,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Sam4udass: When sam meets unsupervised domain adaptive semantic segmentation in intelligent ve- hicles,

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Observation e9f52271-fced-471d-8acd-cf8a1584d203 · outbound

This paper cites Occnerf: Self-supervised multi- camera occupancy prediction with neural radiance fields,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Occnerf: Self-supervised multi- camera occupancy prediction with neural radiance fields,

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Observation a46554fc-03ac-48d2-9dd0-7cc169d2c97d · outbound

This paper cites Open 3D World in Autonomous Driving.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Open 3D World in Autonomous Driving

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Observation f9188c8d-3d07-49f5-bccb-eeb23a512c27 · outbound

This paper cites OVO: Open-Vocabulary Occupancy.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities OVO: Open-Vocabulary Occupancy

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Observation 3efc75fb-904e-4f13-b4a2-75e4c755c603 · outbound

This paper cites Clip2scene: Towards label-efficient 3d scene understanding by clip,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Clip2scene: Towards label-efficient 3d scene understanding by clip,

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Observation 1ed4d6d4-5cfb-4759-88dd-7b6a889fd293 · outbound

This paper cites Vlm2scene: Self- supervised image-text-lidar learning with foundation models for autonomous driving scene understanding,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Vlm2scene: Self- supervised image-text-lidar learning with foundation models for autonomous driving scene understanding,

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Observation 04225145-4aa3-4e2d-b62a-7ca4a398a769 · outbound

This paper cites Unsupervised 3d perception with 2d vision-language distillation for autonomous driv- ing,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Unsupervised 3d perception with 2d vision-language distillation for autonomous driv- ing,

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Observation 27d822af-a0f7-4591-9dcb-8509cecbd4bf · outbound

This paper cites Opensight: A simple open-vocabulary framework for lidar-based object detection,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Opensight: A simple open-vocabulary framework for lidar-based object detection,

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Observation f0edd2f8-565c-4683-b2e1-15f3c2bc8e0a · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

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Observation 6545c1ab-899f-40ff-971a-1f30917478df · outbound

This paper cites GPT-Driver: Learning to Drive with GPT.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities GPT-Driver: Learning to Drive with GPT

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Observation ab1bbacd-b53e-48fb-bb5b-3d99c6f92b34 · outbound

This paper cites OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

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Observation 2c0a3865-ec48-4065-8669-aaee8b2ef20e · outbound

This paper cites Drivegpt4: Interpretable end-to-end autonomous driving via large language model,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Drivegpt4: Interpretable end-to-end autonomous driving via large language model,

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Observation e5bb838d-6f68-4829-a109-84ab3eec627f · outbound

This paper cites Dol- phins: Multimodal language model for driving,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Dol- phins: Multimodal language model for driving,

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Observation 3598c952-d75e-4441-ae7e-319b1ece4b4f · outbound

This paper cites EMMA: End-to-End Multimodal Model for Autonomous Driving.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities EMMA: End-to-End Multimodal Model for Autonomous Driving

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Observation 5e4f1d86-3b82-4191-9958-3027096e23f7 · outbound

This paper cites Lidar-llm: Exploring the potential of large language models for 3d lidar understanding,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Lidar-llm: Exploring the potential of large language models for 3d lidar understanding,

Reference 60

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Observation 1c1f6882-564a-4c43-bfa7-9e01b4c0f605 · outbound

This paper cites A survey on hallucination in large language mod- els: Principles, taxonomy, challenges, and open ques- tions,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities A survey on hallucination in large language mod- els: Principles, taxonomy, challenges, and open ques- tions,

Reference 61

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Observation 977244c2-3572-457e-ba6a-ed1b2b120fdc · outbound

This paper cites Retrieval-augmented genera- tion for knowledge-intensive nlp tasks,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Retrieval-augmented genera- tion for knowledge-intensive nlp tasks,

Reference 62

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Observation 703757cb-f79b-48ae-a467-4b4bd68e965e · outbound

This paper cites Self-rag: Learning to retrieve, generate, and critique through self-reflection,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Self-rag: Learning to retrieve, generate, and critique through self-reflection,

Reference 63

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Observation 73b7e1da-1908-430c-a002-6ffd354230d4 · outbound

This paper cites Driving with llms: Fusing object-level vector modal- ity for explainable autonomous driving,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Driving with llms: Fusing object-level vector modal- ity for explainable autonomous driving,

Reference 64

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Observation 001be390-273c-42c5-9086-c894ec33fd87 · outbound

This paper cites A survey on occupancy perception for autonomous driv- ing: The information fusion perspective,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities A survey on occupancy perception for autonomous driv- ing: The information fusion perspective,

Reference 65

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Observation 1479719f-f425-4895-b9df-12cec0af5abb · outbound

This paper cites Neural vol- umetric world models for autonomous driving,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Neural vol- umetric world models for autonomous driving,

Reference 66

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Observation 34593224-8340-4434-88b9-dcf1ba67067f · outbound

This paper cites Tri-perspective view for vision-based 3d semantic oc- cupancy prediction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Tri-perspective view for vision-based 3d semantic oc- cupancy prediction,

Reference 67

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Observation 82f575c2-6de9-438b-9b6d-0f9a26cb95d1 · outbound

This paper cites V oxformer: Sparse voxel transformer for camera-based 3d se- mantic scene completion,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities V oxformer: Sparse voxel transformer for camera-based 3d se- mantic scene completion,

Reference 68

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Observation 65acb063-59a9-4f83-be6d-2983ad8596a0 · outbound

This paper cites Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction,

Reference 69

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Observation 30f16fba-7139-4035-b7a9-0c136bee422f · outbound

This paper cites Fully sparse 3d occupancy prediction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Fully sparse 3d occupancy prediction,

Reference 70

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Observation 9ffaaa56-33af-47de-b459-9b0f51844400 · outbound

This paper cites Hybridocc: Nerf enhanced transformer-based multi- camera 3d occupancy prediction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Hybridocc: Nerf enhanced transformer-based multi- camera 3d occupancy prediction,

Reference 71

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Observation 4615e532-1b5b-4770-9edd-3de223996b30 · outbound

This paper cites Renderocc: Vision- centric 3d occupancy prediction with 2d rendering supervision,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Renderocc: Vision- centric 3d occupancy prediction with 2d rendering supervision,

Reference 72

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Observation be6922b5-27bf-4b10-a3f6-a4516abf7fb0 · outbound

This paper cites S-nerf++: Autonomous driving simu- lation via neural reconstruction and generation,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities S-nerf++: Autonomous driving simu- lation via neural reconstruction and generation,

Reference 73

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Observation d5c805ad-40d8-4c26-9740-77783c7ae86f · outbound

This paper cites Selfocc: Self-supervised vision-based 3d occupancy prediction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Selfocc: Self-supervised vision-based 3d occupancy prediction,

Reference 74

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Observation a473cd25-a1ab-4d80-8798-767ed4d65a06 · outbound

This paper cites RenderWorld: World Model with Self-Supervised 3D Label.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities RenderWorld: World Model with Self-Supervised 3D Label

Reference 75

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Observation 1d99020e-7f55-40ca-b8d5-8c61fb58add6 · outbound

This paper cites GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow

Reference 76

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Observation 24b1a303-cb16-4c99-a1b9-33942cd6e303 · outbound

This paper cites Street gaussians: Modeling dynamic urban scenes with gaussian splat- ting,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Street gaussians: Modeling dynamic urban scenes with gaussian splat- ting,

Reference 77

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Observation d704555e-22fb-4d26-89bc-67fec162f7df · outbound

This paper cites Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.412278Z

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Observation 2e9902a1-753f-4e91-9b0d-b471be8fa350 · outbound

This paper cites Surroundocc: Multi-camera 3d occupancy pre- diction for autonomous driving,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Surroundocc: Multi-camera 3d occupancy pre- diction for autonomous driving,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.419424Z

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Observation 1c398fed-632f-4b0f-9b3b-a027bdf1a06d · outbound

This paper cites Uno: Unsupervised occupancy fields for per- ception and forecasting,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Uno: Unsupervised occupancy fields for per- ception and forecasting,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.430373Z

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Observation b2d0919c-459d-47d2-9266-e11f47851c21 · outbound

This paper cites Maeli: Masked au- toencoder for large-scale lidar point clouds,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Maeli: Masked au- toencoder for large-scale lidar point clouds,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.440273Z

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Observation a71e55e2-ee72-4f0e-9534-fc3aeab6833b · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Masked autoencoder for self-supervised pre-training on lidar point clouds,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.454199Z

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Observation ad181b6e-cb92-4571-a1b0-7d6603404a52 · outbound

This paper cites Bev-mae: Bird’s eye view masked autoencoders for point cloud pre-training in autonomous driving sce- narios,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Bev-mae: Bird’s eye view masked autoencoders for point cloud pre-training in autonomous driving sce- narios,

Reference 83

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Observation 717ed5c5-dc35-4283-81b9-1c546ed7bbf2 · outbound

This paper cites Geo- mae: Masked geometric target prediction for self- supervised point cloud pre-training,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Geo- mae: Masked geometric target prediction for self- supervised point cloud pre-training,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.958701Z

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Observation 2cb009e3-6fad-4b92-af25-a0e5f5d355db · outbound

This paper cites Openoccupancy: A large scale benchmark for surrounding semantic oc- cupancy perception,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Openoccupancy: A large scale benchmark for surrounding semantic oc- cupancy perception,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.853318Z

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Observation f6a9f70f-24ed-4655-8ec9-92d3e3846131 · outbound

This paper cites Occ3d: A large-scale 3d occu- pancy prediction benchmark for autonomous driving,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Occ3d: A large-scale 3d occu- pancy prediction benchmark for autonomous driving,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.741657Z

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Observation f207edd4-de38-4471-a912-3265676c83f5 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Representation Learning with Contrastive Predictive Coding

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.484270Z

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Observation 64004ea8-13af-4853-be77-bf72b6e0d902 · outbound

This paper cites Cross-modal contrastive learning for domain adap- tation in 3d semantic segmentation,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Cross-modal contrastive learning for domain adap- tation in 3d semantic segmentation,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.602627Z

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Observation 17f96b34-aa93-4c4c-bd53-8196ce44d4eb · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities 4d contrastive superflows are dense 3d representation learners,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.493586Z

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Observation e5ae1d23-a4cc-42f0-96be-6a0d24730c45 · outbound

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Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Superflow++: Enhanced spatiotemporal con- sistency for cross-modal data pretraining,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.409874Z

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Observation e51c080a-0dbe-4bfd-a2bc-371a2c3211e6 · outbound

This paper cites ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-04T20:52:06.510402Z

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Observation f7d3ad64-de83-4a49-9c0b-b076dc108545 · outbound

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

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection

Reference 92

Resolution: verified exact; local_arxiv, observed 2026-08-04T20:52:07.308999Z

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Observation 1fe045da-f0f8-46b0-8cf4-87fc20ed479b · outbound

This paper cites Distill- bev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Distill- bev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.268784Z

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Observation e5b5c084-ceb6-4252-9a09-6123e0307ead · outbound

This paper cites Geometric-aware Pretraining for Vision-centric 3D Object Detection.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Geometric-aware Pretraining for Vision-centric 3D Object Detection

Reference 94

Resolution: verified exact; local_arxiv, observed 2026-08-04T20:52:07.279648Z

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Observation 6b67b156-6f08-49d4-8398-ee7bd8d43878 · outbound

This paper cites Unidis- till: A universal cross-modality knowledge distillation framework for 3d object detection in bird’s-eye view,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Unidis- till: A universal cross-modality knowledge distillation framework for 3d object detection in bird’s-eye view,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:11.129176Z

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Observation 2c0bd976-ee51-4a9c-8764-b69dcaafee5c · outbound

This paper cites Revisiting domain generalized stereo match- ing networks from a feature consistency perspec- tive,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Revisiting domain generalized stereo match- ing networks from a feature consistency perspec- tive,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:10.988761Z

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Observation aa4d1f90-2d62-4d39-93f3-c3222569a5c0 · outbound

This paper cites Weakly supervised monocular 3d object detection using multi-view projection and direction consis- tency,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Weakly supervised monocular 3d object detection using multi-view projection and direction consis- tency,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:10.852859Z

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Observation 60efd1e8-6ef1-4470-9b09-ffaf730b17a8 · outbound

This paper cites Bevformer: learning bird’s-eye- view representation from lidar-camera via spatiotem- poral transformers,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Bevformer: learning bird’s-eye- view representation from lidar-camera via spatiotem- poral transformers,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:10.737724Z

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Observation 13f62e17-857c-4cc5-8769-e33ca9238a43 · outbound

This paper cites Ega- depth: Efficient guided attention for self-supervised multi-camera depth estimation,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Ega- depth: Efficient guided attention for self-supervised multi-camera depth estimation,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:10.597236Z

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Observation e79ebaf7-1f1e-4b72-991a-0aa03f6a2c33 · outbound

This paper cites Multimae: Multi-modal multi-task masked autoen- coders,.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Multimae: Multi-modal multi-task masked autoen- coders,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:52:10.430743Z

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