Search Results for author:
Found 9 papers, 3 papers with code
End-to-End Training of Hybrid CNN-CRF Models for Stereo
We propose a novel and principled hybrid CNN+CRF model for stereo estimation.
Scalable Full Flow with Learned Binary Descriptors
We tackle the computation- and memory-intensive operations on the 4D cost volume by a min-projection which reduces memory complexity from quadratic to linear and binary descriptors for efficient matching.
Learning Energy Based Inpainting for Optical Flow
Modern optical flow methods are often composed of a cascade of many independent steps or formulated as a black box neural network that is hard to interpret and analyze.
Self-Supervised Learning for Stereo Reconstruction on Aerial Images
Recent developments established deep learning as an inevitable tool to boost the performance of dense matching and stereo estimation.
Learned Collaborative Stereo Refinement
The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space.
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models.
Frame-To-Frame Consistent Semantic Segmentation
In this work, we aim for temporally consistent semantic segmentation throughout frames in a video.
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo
We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF.
InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization
We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features.
Ranked #1 on
Unsupervised Semantic Segmentation
on COCO-Stuff-15
