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Found 13 papers, 10 papers with code
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow.
Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
In our approach, we render diverse view maps from BEV features and rectify the perspective bias of these maps, leveraging implicit foreground volumes to bridge the camera and BEV planes.
OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
The vision-based perception for autonomous driving has undergone a transformation from the bird-eye-view (BEV) representations to the 3D semantic occupancy.
3D Semantic Occupancy Prediction
3D Semantic Scene Completion from a single RGB image
+3
A Simple Baseline for Supervised Surround-view Depth Estimation
The former is achieved by the self-attention module within each view, while the latter is realized by the adjacent attention module, which computes the attention across multi-cameras to exchange the multi-scale representations across surround-view feature maps.
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark.
Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction
To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively.
Ranked #1 on
Prediction Of Occupancy Grid Maps
on nuScenes
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark
To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving.
A Simple Baseline for Multi-Camera 3D Object Detection
First, we extract multi-scale features and generate the perspective object proposals on each monocular image.
Motion Gait: Gait Recognition via Motion Excitation
Gait recognition, which can realize long-distance and contactless identification, is an important biometric technology.
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
Ranked #15 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Dimension Embeddings for Monocular 3D Object Detection
In this paper, we propose a general method to learn appropriate embeddings for dimension estimation in monocular 3D object detection.
Objects are Different: Flexible Monocular 3D Object Detection
The precise localization of 3D objects from a single image without depth information is a highly challenging problem.
Ranked #8 on
Monocular 3D Object Detection
on KITTI Cars Moderate
