CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining | Paper Citation Record

Observation 062fba49-2154-4252-83a3-581a32873d73 · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining nuscenes: A multi- modal dataset for autonomous driving,

Reference 1

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Observation 7ecf7c28-bd24-49b1-8165-d411f97e777a · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Scalability in perception for autonomous driving: Waymo open dataset,

Reference 2

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Observation e751352a-7adf-40a6-8a8e-0edc9b70dfb5 · outbound

This paper cites Planning-oriented autonomous driving,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Planning-oriented autonomous driving,

Reference 3

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Observation 1564b81e-4c0c-400d-9309-016e581a3306 · outbound

This paper cites Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,

Reference 4

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Observation cb2a1b19-dbb0-4122-b56e-3ea5ffb96dc5 · outbound

This paper cites Deep learning-based perception systems for autonomous driving: A comprehensive survey,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Deep learning-based perception systems for autonomous driving: A comprehensive survey,

Reference 5

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Observation 3e2c7ddb-e386-4aa1-a736-d739c4624cf5 · outbound

This paper cites Towards deep radar perception for autonomous driving: Datasets, methods, and challenges,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Towards deep radar perception for autonomous driving: Datasets, methods, and challenges,

Reference 6

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Observation 4af08331-9973-44cd-afc5-64b636825662 · outbound

This paper cites Crkd: Enhanced camera-radar object detection with cross-modality knowledge distillation,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Crkd: Enhanced camera-radar object detection with cross-modality knowledge distillation,

Reference 7

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Observation fcf1dbb4-ef65-4bee-a573-a9f8fa714553 · outbound

This paper cites Multi-modal 3d object detection in autonomous driving: a survey,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Multi-modal 3d object detection in autonomous driving: a survey,

Reference 8

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Observation 934cff3c-d8f9-4df0-9063-6e9481f8daee · outbound

This paper cites Vision meets robotics: The kitti dataset,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Vision meets robotics: The kitti dataset,

Reference 9

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Observation 7fc45cef-bd46-4528-a107-612ff3885da5 · outbound

This paper cites Unifying voxel-based representation with transformer for 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Unifying voxel-based representation with transformer for 3d object detection,

Reference 10

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Observation 0a0b34e7-1b7f-4baa-877f-4f9515664ecf · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,

Reference 11

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Observation ac29c394-c801-4c6a-9e0d-fa7f5075a35d · outbound

This paper cites Futr3d: A unified sensor fusion framework for 3d detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Futr3d: A unified sensor fusion framework for 3d detection,

Reference 12

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Observation e0d4e467-6f96-413a-8252-193e487885c0 · outbound

This paper cites Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey

Reference 13

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Observation cebbb4c0-49ea-43e3-b4e2-1bff689c94d5 · outbound

This paper cites Centerfusion: Center-based radar and camera fusion for 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Centerfusion: Center-based radar and camera fusion for 3d object detection,

Reference 14

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Observation 91ed6553-2435-4bef-b91c-60a6528c5093 · outbound

This paper cites BEV-Guided Multi-Modality Fusion for Driving Perception,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining BEV-Guided Multi-Modality Fusion for Driving Perception,

Reference 15

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Observation 05fc51d7-13d8-450b-8862-2b020a608998 · outbound

This paper cites Crn: Camera radar net for accurate, robust, efficient 3d perception,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Crn: Camera radar net for accurate, robust, efficient 3d perception,

Reference 16

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Observation 8eb52c3c-7d2f-4fa3-b1d9-d7514f081be3 · outbound

This paper cites Bevcar: Camera-radar fusion for bev map and object segmentation,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Bevcar: Camera-radar fusion for bev map and object segmentation,

Reference 17

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Observation 00c05714-2f14-47c4-ae52-4f2d43ce85ff · outbound

This paper cites Licrocc: Teach radar for accurate semantic occupancy prediction using lidar and camera,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Licrocc: Teach radar for accurate semantic occupancy prediction using lidar and camera,

Reference 18

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Observation 0800e28f-1b25-4abc-81db-b4d3728d8380 · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,

Reference 19

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Observation 304d7ce1-644c-4537-a69a-4da10459b167 · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,

Reference 20

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Observation 322f5d86-c684-4336-8869-f8b90eacf7b6 · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Gd-mae: generative decoder for mae pre-training on lidar point clouds,

Reference 21

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Observation bba2c985-3fbe-442e-9e91-0468e4c85a60 · outbound

This paper cites Unipad: A universal pre-training paradigm for autonomous driving,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Unipad: A universal pre-training paradigm for autonomous driving,

Reference 22

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Observation 4d58cf56-2753-4e0f-baef-b3eb90f5f726 · outbound

This paper cites Visual point cloud forecasting enables scalable autonomous driving,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Visual point cloud forecasting enables scalable autonomous driving,

Reference 23

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Observation 99b02e2e-4e30-4fe4-b256-2aa84fdd1ce2 · outbound

This paper cites Forging spatial intelligence: A roadmap of multi-modal data pre- training for autonomous systems,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Forging spatial intelligence: A roadmap of multi-modal data pre- training for autonomous systems,

Reference 24

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Observation c6898dc8-6f24-495a-a8b0-c1e20098a62c · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Masked autoencoder for self-supervised pre-training on lidar point clouds,

Reference 25

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Observation 0006f65d-dda1-4cf4-8d34-f402a52157d9 · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Bev-mae: Bird’s eye view masked autoencoders for point cloud pre-training in autonomous driving scenarios,

Reference 26

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Observation cd5233cc-fe29-4734-a119-42b6ef0e4f4d · outbound

This paper cites Is pseudo- lidar needed for monocular 3d object detection?.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Is pseudo- lidar needed for monocular 3d object detection?

Reference 27

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Observation 39df89d9-ffd3-4f4f-ad55-a2d23c765896 · outbound

This paper cites Pimae: Point cloud and image interactive masked autoencoders for 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Pimae: Point cloud and image interactive masked autoencoders for 3d object detection,

Reference 28

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Observation 2324b04e-cca9-4898-ac0a-ecbfbe070659 · outbound

This paper cites Point cloud forecasting as a proxy for 4d occupancy forecasting,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Point cloud forecasting as a proxy for 4d occupancy forecasting,

Reference 29

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Observation db276bf3-a01a-4839-a03c-3a972a2ae897 · outbound

This paper cites UniWorld: Autonomous Driving Pre-training via World Models.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining UniWorld: Autonomous Driving Pre-training via World Models

Reference 30

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Observation 964ab74a-477c-4f8b-90ce-6a789f4a4b70 · outbound

This paper cites Driveworld: 4d pre- trained scene understanding via world models for autonomous driving,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Driveworld: 4d pre- trained scene understanding via world models for autonomous driving,

Reference 31

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Observation 72006e77-9a10-44b5-93c6-7e6a66738a99 · outbound

This paper cites Crt-fusion: Camera, radar, temporal fusion using motion information for 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Crt-fusion: Camera, radar, temporal fusion using motion information for 3d object detection,

Reference 32

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Observation 5879092a-c1fe-49cc-bba2-38b39952cabe · outbound

This paper cites Cross-modality knowledge distillation network for monocular 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Cross-modality knowledge distillation network for monocular 3d object detection,

Reference 33

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Observation 00c5814f-4724-4f08-80c1-d6b99386c498 · outbound

This paper cites X-align: Cross-modal cross-view alignment for bird’s-eye-view segmentation,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining X-align: Cross-modal cross-view alignment for bird’s-eye-view segmentation,

Reference 34

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Observation e81bfd8c-db19-4058-84e6-365b16beb067 · outbound

This paper cites Unifying voxel-based representation with transformer for 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Unifying voxel-based representation with transformer for 3d object detection,

Reference 35

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Observation e85eebda-ed7d-4620-9866-de419687b7aa · outbound

This paper cites Masked au- toencoders are scalable vision learners,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Masked au- toencoders are scalable vision learners,

Reference 36

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Observation bf3b9b43-17d9-4164-a122-d06db045b272 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Point-bert: Pre-training 3d point cloud transformers with masked point modeling,

Reference 37

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Observation 858884ce-d266-417c-b1e8-613a285df5d9 · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Masked autoencoders for point cloud self-supervised learning,

Reference 38

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Observation 335f2fe5-99c6-4d37-ba35-5f8495595ff1 · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection,

Reference 39

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Observation 2e94a117-8695-4af8-ab03-802e1e9a4554 · outbound

This paper cites Visionpad: A vision-centric pre-training paradigm for autonomous driving,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Visionpad: A vision-centric pre-training paradigm for autonomous driving,

Reference 40

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Observation 28933d85-e0ad-426d-bae8-c298d908c191 · outbound

This paper cites Bootstrapping autonomous driving radars with self-supervised learn- ing,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Bootstrapping autonomous driving radars with self-supervised learn- ing,

Reference 41

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Observation 807615f1-767c-4ceb-8282-76bb6c1003ea · outbound

This paper cites Self-supervised sparse sensor fusion for long range percep- tion,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Self-supervised sparse sensor fusion for long range percep- tion,

Reference 42

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Observation d7015175-c733-4c9a-9618-126bff329aed · outbound

This paper cites Multi-sensor fusion in automated driving: A survey,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Multi-sensor fusion in automated driving: A survey,

Reference 43

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Observation 16b50faf-2f58-4de9-914b-88ca657d651c · outbound

This paper cites Radar-camera fusion for object detection and semantic segmentation in autonomous driving: A comprehensive review,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Radar-camera fusion for object detection and semantic segmentation in autonomous driving: A comprehensive review,

Reference 44

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Observation 738ddddb-499d-4da1-b27d-e84f6200fbef · outbound

This paper cites Cramnet: Camera-radar fusion with ray-constrained cross-attention for robust 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Cramnet: Camera-radar fusion with ray-constrained cross-attention for robust 3d object detection,

Reference 45

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Observation d961c668-6e69-4e39-98f3-7095d73fff3f · outbound

This paper cites Mvfusion: Multi-view 3d object detection with semantic-aligned radar and camera fusion,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Mvfusion: Multi-view 3d object detection with semantic-aligned radar and camera fusion,

Reference 46

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Observation d29ed390-1250-4158-97ab-2fb5bea753df · outbound

This paper cites EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection

Reference 47

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Observation c4536b1d-f098-4986-8599-9a7a7c43c7ce · outbound

This paper cites Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,

Reference 48

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Observation a09c2691-f83a-41ed-aad7-ab5317dbfa52 · outbound

This paper cites Sparc-ad: A baseline for radar-camera fusion in end-to-end autonomous driving,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Sparc-ad: A baseline for radar-camera fusion in end-to-end autonomous driving,

Reference 49

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Observation adf4a1c5-6851-4433-b54b-8de23a192288 · outbound

This paper cites HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation

Reference 50

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Observation 69a3f444-99c5-4e16-83bc-76bdc7d437b3 · outbound

This paper cites Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free,

Reference 51

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Observation cc6822de-a09a-403d-bcdd-e6c34f84ac4b · outbound

This paper cites Deep residual learning for image recognition,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Deep residual learning for image recognition,

Reference 52

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Observation 01de08d9-d58c-4beb-b7ed-b19f3705b91f · outbound

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

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Fcos3d: Fully convolutional one- stage monocular 3d object detection,

Reference 53

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This paper cites Feature pyramid networks for object detection,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Feature pyramid networks for object detection,

Reference 54

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Observation b9176a1c-c29d-4909-bd7f-019569d38310 · outbound

This paper cites Pointpillars: Fast encoders for object detection from point clouds,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Pointpillars: Fast encoders for object detection from point clouds,

Reference 55

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Observation 6d3aa147-eb36-428a-b164-6ce086c0082b · outbound

This paper cites Bevformer: learning bird’s-eye-view representation from lidar-camera JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 17 via spatiotemporal transformers,.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining Bevformer: learning bird’s-eye-view representation from lidar-camera JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 17 via spatiotemporal transformers,

Reference 56

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Observation d98dfb6f-0704-49f5-928d-b13aab3f06ca · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 57

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