Stereo Depth Estimation
State-of-the-art 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.
Real-time self-adaptive deep stereo
CVPR 2019
• CVLAB-Unibo/Real-time-self-adaptive-deep-stereo
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Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs.
Anytime Stereo Image Depth Estimation on Mobile Devices
• mileyan/AnyNet
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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
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
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.