Stereo Depth Estimation
Leaderboards
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Greatest papers with code
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.
Anytime Stereo Image Depth Estimation on Mobile Devices
• mileyan/AnyNet
• 
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
SOTA for Stereo Depth Estimation on KITTI2012
Real-time self-adaptive deep stereo
CVPR 2019
• CVLAB-Unibo/Real-time-self-adaptive-deep-stereo
• 
Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs.
Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve
In contrast, generic camera models allow for very accurate calibration due to their flexibility.
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
ICLR 2020
• mileyan/Pseudo_Lidar_V2
•
In this paper, we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
3D OBJECT DETECTION FROM STEREO IMAGES AUTONOMOUS DRIVING STEREO DEPTH ESTIMATION
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.
3D OBJECT DETECTION AUTONOMOUS DRIVING STEREO DEPTH ESTIMATION
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
CVPR 2019
• baidu-research/UnDepthflow
•
In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption.
MOTION SEGMENTATION OPTICAL FLOW ESTIMATION STEREO DEPTH ESTIMATION VISUAL ODOMETRY
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images.
Learning to Adapt for Stereo
CVPR 2019
• CVLAB-Unibo/Learning2AdaptForStereo
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Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment.
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation
• daiszh/A-TVSNet
•
We propose a learning-based network for depth map estimation from multi-view stereo (MVS) images.