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Awesome papers about Multi-Camera 3D Object Detection and Segmentation in Bird-Eye-View, such as DETR3D, BEVDet, BEVFormer
awesome-bev-perception-multi-cameras's Introduction
Awesome BEV Perception from Multi-Cameras
- LSS: Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D [paper] [Github]
- DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries [paper] [Github]
- FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras [paper] [Github]
- CVT: Cross-view Transformers for real-time Map-view Semantic Segmentation [paper] [Github]
- Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection [paper]
- BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers [paper] [Github]
- PETR: Position Embedding Transformation for Multi-View 3D Object Detection [paper][Github]
- ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [paper][Github]
- SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection from Multi-View Camera Images with Global Cross-Sensor Attention[paper] [Github]
- LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation [paper] [Github]
- BEVSegFormer: Bird’s Eye View Semantic Segmentation From Arbitrary Camera Rigs [paper]
- BEVDet: High-Performance Multi-Camera 3D Object Detection in Bird-Eye-View [paper] [Github]
- BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection [paper]
- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images [paper][Github]
- M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation [paper]
- BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving [paper] [Github]
- PolarDETR: Polar Parametrization for Vision-based Surround-View 3D Detection[paper] [Github]
- (AAAI 2023) PolarFormer: Multi-camera 3D Object Detection with Polar Transformers[paper] [Github]
- CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection[paper] [Github]
- (AAAI 2023) BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection [paper][Github]
- A Simple Baseline for BEV Perception Without LiDAR [paper] [Github]
- BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision [paper]
- AeDet: Azimuth-invariant Multi-view 3D Object Detection [paper] [Github]
- (AAAI 2023) BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo [paper] [Github]
- Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection [paper][Github]
- STS: Surround-view Temporal Stereo for Multi-view 3D Detection [paper]
- (ICLR 2023) BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection [paper] [Github]
- TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning [paper][Github]
- Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline [paper] [Github]
- MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception [paper][Github]
- (ICRA 2022) HDMapNet: An Online HD Map Construction and Evaluation Framework [paper] [Github]
- (ICLR 2023) MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction [paper] [Github]
- FUTR3D: A Unified Sensor Fusion Framework for 3D Detection [paper] [Github]
- (NeurIPS 2022) BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework [paper] [Github]
- (NeurIPS 2022) Unifying Voxel-based Representation with Transformer for 3D Object Detection [paper] [Github]
- BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [paper] [Github]
- CMT: Cross Modal Transformer via Coordinates Encoding for 3D Object Dectection [paper] [Github]
- Vision-Centric BEV Perception: A Survey [paper] [Github]
- Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe [paper][Github]
- TPVFormer: An academic alternative to Tesla's Occupancy Network [Github]
- Voxel-MAE: Masked Autoencoders for Self-supervised Pre-training Large-scale Point Clouds [paper][Github]
- BEV-MAE: Bird's Eye View Masked Autoencoders for Outdoor Point Cloud Pre-training[paper][Github]
- aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception [paper] [Github]
- Focal Sparse Convolutional Networks for 3D Object Detection [paper] [Github]
- Voxel Field Fusion for 3D Object Detection [paper] [Github]
- Scaling up Kernels in 3D CNNs [paper] [Github]
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