Search Results for author:
Found 12 papers, 4 papers with code
Graph Cut based Continuous Stereo Matching using Locally Shared Labels
We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts.
Superdifferential Cuts for Binary Energies
We then present our method as generalization of SSP, which is further shown to generalize several state-of-the-art techniques for higher-order and pairwise non-submodular functions [Ayed13, Gorelick14, Tang14].
Continuous 3D Label Stereo Matching using Local Expansion Moves
The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.
Joint Recovery of Dense Correspondence and Cosegmentation in Two Images
We propose a new technique to jointly recover cosegmentation and dense per-pixel correspondence in two images.
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
This unified framework benefits all four tasks - stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency.
Semi-Global Stereo Matching with Surface Orientation Priors
In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes.
Neural Inverse Rendering for General Reflectance Photometric Stereo
We present a novel convolutional neural network architecture for photometric stereo (Woodham, 1980), a problem of recovering 3D object surface normals from multiple images observed under varying illuminations.
Path Planning using Neural A* Search
We present Neural A*, a novel data-driven search method for path planning problems.
Neural Structure Fields with Application to Crystal Structure Autoencoders
We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks.
Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors.
A Transformer Model for Symbolic Regression towards Scientific Discovery
Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra computational cost.
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science.
