3D Object Detection From Stereo Images
12 papers with code • 3 benchmarks • 4 datasets
Estimating oriented 3D bounding boxes from Stereo Cameras only.
Image: You et al
Benchmarks
These leaderboards are used to track progress in 3D Object Detection From Stereo Images
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KITTI
IBISCape
3D-POP
Most implemented papers
Stereo R-CNN based 3D Object Detection for Autonomous Driving
HKUST-Aerial-Robotics/Stereo-RCNN
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CVPR 2019
Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
mileyan/pseudo_lidar
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CVPR 2019
However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference.
Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
Zengyi-Qin/TLNet
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CVPR 2019
In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information.
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
mileyan/Pseudo_Lidar_V2
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ICLR 2020
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
DSGN: Deep Stereo Geometry Network for 3D Object Detection
chenyilun95/DSGN
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CVPR 2020
Most state-of-the-art 3D object detectors heavily rely on LiDAR sensors because there is a large performance gap between image-based and LiDAR-based methods.
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images.
3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF.
Wasserstein Distances for Stereo Disparity Estimation
Div99/W-Stereo-Disp
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NeurIPS 2020
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
PLUMENet: Efficient 3D Object Detection from Stereo Images
In this paper we propose a model that unifies these two tasks and performs them in the same metric space.
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
Owen-Liuyuxuan/visualDet3D
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Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs.
