Robust 3D Object Detection
11 papers with code • 2 benchmarks • 2 datasets
3D Object Detection under Out-of-Distribution Scenarios
Benchmarks
These leaderboards are used to track progress in Robust 3D Object Detection
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Libraries
Use these libraries to find Robust 3D Object Detection models and implementations
nuScenes-C
Most implemented papers
PointPillars: Fast Encoders for Object Detection from Point Clouds
nutonomy/second.pytorch
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CVPR 2019
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
sshaoshuai/PointRCNN
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CVPR 2019
In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
open-mmlab/OpenPCDet
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CVPR 2020
We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
Center-based 3D Object Detection and Tracking
tianweiy/CenterPoint
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CVPR 2021
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
TANet: Robust 3D Object Detection from Point Clouds with Triple Attention
In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches.
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection.
LiDAR Snowfall Simulation for Robust 3D Object Detection
syscv/lidar_snow_sim
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CVPR 2022
Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds.
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection.
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
ldkong1205/Robo3D
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ICCV 2023
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications.
Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation
Specifically, spatial alignment is proposed to deal with the geometry discrepancy for better instance matching between LiDAR and radar.
