Search Results for author:
Found 8 papers, 4 papers with code
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured.
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images.
Multi-sensor large-scale dataset for multi-view 3D reconstruction
We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks.
Unpaired Depth Super-Resolution in the Wild
We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps.
How Good MVSNets Are at Depth Fusion
We study the effects of the additional input to deep multi-view stereo methods in the form of low-quality sensor depth.
Deep Vectorization of Technical Drawings
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images.
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.
Perceptual deep depth super-resolution
RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common.
