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Found 7 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.
Sphere-Guided Training of Neural Implicit Surfaces
In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction.
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.
Adversarial Generation of Continuous Images
In most existing learning systems, images are typically viewed as 2D pixel arrays.
Ranked #12 on
Image Generation
on LSUN Churches 256 x 256
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios.
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.
