Search Results for author:
Found 12 papers, 4 papers with code
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement
We propose CHOSEN, a simple yet flexible, robust and effective multi-view depth refinement framework.
OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas
Immersive maps such as Google Street View and Bing Streetside provide true-to-life views with a massive collection of panoramas.
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.
Ranked #1 on
Stereo Depth Estimation
on KITTI2015
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets.
PatchFCN for Intracranial Hemorrhage Detection
This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans.
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
To minimize the number of cameras needed for surround perception, we utilize fisheye cameras.
Learning a Multi-View Stereo Machine
We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches as well as recent learning based methods.
Hierarchical Surface Prediction for 3D Object Reconstruction
A major limitation of such approaches is that they only predict a coarse resolution voxel grid, which does not capture the surface of the objects well.
Learning the Matching Function
In this paper we propose a method, which learns the matching function, that automatically finds the space of allowed changes in visual appearance, such as due to the motion blur, chromatic distortions, different colour calibration or seasonal changes.
Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise Linear Priors
The number of unknowns is O(LK) per pairwise clique in terms of the state space size $L$ and the number of linear segments K. This compares to an O(L^2) size complexity of the standard LP relaxation if the piecewise linear structure is ignored.
