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
Latest papers
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
First, to effectively lift the 2D information to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser connections and extracts depth-guided features.
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector
Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10. 44%, 5. 69%, 5. 97% mAP respectively on the official KITTI benchmark.
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
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.
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.
Wasserstein Distances for Stereo Disparity Estimation
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
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.
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.
DSGN: Deep Stereo Geometry Network for 3D Object Detection
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.
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
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.