6 citations · 8 across the 6 of their papers we have counts for
9 papers
ATATA: One Algorithm to Align Them All
Boyi Pang, Savva Ignatyev, Vladimir Ippolitov +8
We suggest a new multi-modal algorithm for joint inference of paired structurally aligned samples with Rectified Flow models. While some existing methods propose a codependent gene…
A3D: Does Diffusion Dream about 3D Alignment?
Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych +9
We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically…
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
Savva Ignatyev, Daniil Selikhanovych, Oleg Voynov +4
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured. Our approach builds on two recent developments…
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
Yue Fan, Ningjing Fan, Ivan Skorokhodov +5
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additiona…
Sphere-Guided Training of Neural Implicit Surfaces
Andreea Dogaru, Andrei Timotei Ardelean, Savva Ignatyev +2
In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marc…
How Good MVSNets Are at Depth Fusion
Oleg Voynov, Aleksandr Safin, Savva Ignatyev +1
We study the effects of the additional input to deep multi-view stereo methods in the form of low-quality sensor depth. We modify two state-of-the-art deep multi-view stereo method…