Search Results for author: Christian Häne

Found 10 papers, 4 papers with code

Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

no code implementations ECCV 2020 Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.

Depth Estimation Stereo Matching

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

5 code implementations CVPR 2021 Vladimir Tankovich, Christian Häne, yinda zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz

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.

Stereo Depth Estimation Stereo Disparity Estimation +1

Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

no code implementations31 Mar 2020 Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.

Depth Estimation Stereo Matching

Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection

no code implementations8 Sep 2018 Wei-cheng Kuo, Christian Häne, Esther Yuh, Pratik Mukherjee, Jitendra Malik

Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets.

Active Learning Computed Tomography (CT)

PatchFCN for Intracranial Hemorrhage Detection

no code implementations8 Jun 2018 Wei-cheng Kuo, Christian Häne, Esther Yuh, Pratik Mukherjee, Jitendra Malik

This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans.

Computed Tomography (CT) Object Detection +1

Learning a Multi-View Stereo Machine

1 code implementation NeurIPS 2017 Abhishek Kar, Christian Häne, Jitendra Malik

We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches as well as recent learning based methods.

3D Reconstruction

Hierarchical Surface Prediction for 3D Object Reconstruction

1 code implementation3 Apr 2017 Christian Häne, Shubham Tulsiani, Jitendra Malik

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.

3D Object Reconstruction

Learning the Matching Function

no code implementations2 Feb 2015 Ľubor Ladický, Christian Häne, Marc Pollefeys

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.

Optical Flow Estimation Stereo Matching +1

Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise Linear Priors

no code implementations14 Aug 2013 Christopher Zach, Christian Häne

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.

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