Search Results for author:
Found 7 papers, 2 papers with code
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement
We propose CHOSEN, a simple yet flexible, robust and effective multi-view depth refinement framework.
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
We introduce a method for instance proposal generation for 3D point clouds.
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
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
Low Compute and Fully Parallel Computer Vision With HashMatch
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems
Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision.
HyperDepth: Learning Depth From Structured Light Without Matching
We contribute an algorithm for solving this correspondence problem efficiently, without compromising depth accuracy.
