Benchmarks
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Datasets
Greatest papers with code
Learning for Disparity Estimation through Feature Constancy
CVPR 2018
• JiaRenChang/PSMNet
• 
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
Embedded real-time stereo estimation via Semi-Global Matching on the GPU
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles.
Superpixel Segmentation with Fully Convolutional Networks
CVPR 2020
• fuy34/superpixel_fcn
•
In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.
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.
Ranked #14 on
Vehicle Pose Estimation
on KITTI Cars Hard
3D OBJECT DETECTION 3D OBJECT DETECTION FROM STEREO IMAGES DISPARITY ESTIMATION VEHICLE POSE ESTIMATION
Understanding and Robustifying Differentiable Architecture Search
ICLR 2020
• automl/RobustDARTS
•
Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem.
DISPARITY ESTIMATION IMAGE CLASSIFICATION LANGUAGE MODELLING
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
• DeepMotionAIResearch/DenseMatchingBenchmark
•
However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
• Owen-Liuyuxuan/visualDet3D
•
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.
FADNet: A Fast and Accurate Network for Disparity Estimation
• HKBU-HPML/FADNet
•
Deep neural networks (DNNs) have achieved great success in the area of computer vision.
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
CVPR 2016
• HKBU-HPML/FADNet
•
By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
DISPARITY ESTIMATION OPTICAL FLOW ESTIMATION SCENE FLOW ESTIMATION
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
2D OBJECT DETECTION 3D OBJECT DETECTION AUTONOMOUS DRIVING DISPARITY ESTIMATION