18 citations · 30 across the 6 of their papers we have counts for
6 papers · 1 filter
Stereo Risk: A Continuous Modeling Approach to Stereo Matching
Ce Liu, Suryansh Kumar, Shuhang Gu +3
We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision. As it is well-known that stereo matching boils down to a p…
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images
Nishant Jain, Suryansh Kumar, Luc Van Gool
We introduce an improved solution to the neural image-based rendering problem in computer vision. Given a set of images taken from a freely moving camera at train time, the propose…
Neural Implicit Dense Semantic SLAM
Yasaman Haghighi, Suryansh Kumar, Jean-Philippe Thiran +1
Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment us…
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
Ce Liu, Suryansh Kumar, Shuhang Gu +2
We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using…
Uncertainty-Driven Dense Two-View Structure from Motion
Weirong Chen, Suryansh Kumar, Fisher Yu
This work introduces an effective and practical solution to the dense two-view structure from motion (SfM) problem. One vital question addressed is how to mindfully use per-pixel o…
Multi-body Non-rigid Structure-from-Motion
Suryansh Kumar, Yuchao Dai, Hongdong Li
Conventional structure-from-motion (SFM) research is primarily concerned with the 3D reconstruction of a single, rigidly moving object seen by a static camera, or a static and rigi…