Search Results for author:
Found 12 papers, 10 papers with code
PatchmatchNet: Learned Multi-View Patchmatch Stereo
We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo.
Ranked #10 on
Point Clouds
on Tanks and Temples
HoloLens 2 Research Mode as a Tool for Computer Vision Research
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
We present a new, massively parallel method for high-quality multiview matching.
Ranked #22 on
3D Reconstruction
on DTU
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion
We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way.
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo
We present IterMVS, a new data-driven method for high-resolution multi-view stereo.
Learned Multi-Patch Similarity
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
Learned Spectral Super-Resolution
We describe a novel method for blind, single-image spectral super-resolution.
Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks
While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data.
Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction
By training from known points in the same image, the prediction is specifically tailored to the materials and lighting conditions of the particular scene, as well as to the precise camera viewpoint.
A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task.
