Stereo Matching Hand
35 papers with code • 0 benchmarks • 6 datasets
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Latest papers with no code
Object-Centric Stereo Matching for 3D Object Detection
The issue with existing stereo matching networks is that they are designed for disparity estimation, not 3D object detection; the shape and accuracy of object point clouds are not the focus.
Real-Time Variational Fisheye Stereo without Rectification and Undistortion
We also propose a fast way of generating the trajectory field without increasing the processing time compared to conventional rectified methods.
Learning Residual Flow as Dynamic Motion from Stereo Videos
Based on rigid projective geometry, the estimated stereo depth is used to guide the camera motion estimation, and the depth and camera motion are used to guide the residual flow estimation.
Robust Full-FoV Depth Estimation in Tele-wide Camera System
In this paper, to address the above problems we propose a hierarchical hourglass network for robust full-FoV depth estimation in tele-wide camera system, which combines the robustness of traditional stereo-matching methods with the accuracy of DNN.
Multi-Spectral Visual Odometry without Explicit Stereo Matching
Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.
Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors
In this paper, we propose a novel method for event lifetime estimation of stereo event-cameras, allowing generation of sharp gradient images of events that serve as input to disparity estimation methods.
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching
To tackle this problem, we propose a network for disparity estimation based on abundant contextual details and semantic information, called Multi-scale Features Network (MSFNet).
Appearance and Shape from Water Reflection
In other words, for the first time, we show that capturing a direct and water-reflected scene in a single exposure forms a self-calibrating HDR catadioptric stereo camera.
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching
In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide angle lens, and the second FOV is contained in the first FOV and is captured by a tele zoom lens.
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios
Great progress has been made on estimating disparity maps from stereo images.