Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Wewer, Christopher"'
We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data. Recent research heavily focuses on 3D reconstruction using
Externí odkaz:
http://arxiv.org/abs/2408.16544
We aim to tackle sparse-view reconstruction of a 360 3D scene using priors from latent diffusion models (LDM). The sparse-view setting is ill-posed and underconstrained, especially for scenes where the camera rotates 360 degrees around a point, as no
Externí odkaz:
http://arxiv.org/abs/2405.16517
We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture. Existing methods for generalizable 3D reconstruction either do not scale to large scene
Externí odkaz:
http://arxiv.org/abs/2403.16292
Controllable generation of 3D assets is important for many practical applications like content creation in movies, games and engineering, as well as in AR/VR. Recently, diffusion models have shown remarkable results in generation quality of 3D object
Externí odkaz:
http://arxiv.org/abs/2312.14124
Reconstructing dynamic objects from monocular videos is a severely underconstrained and challenging problem, and recent work has approached it in various directions. However, owing to the ill-posed nature of this problem, there has been no solution t
Externí odkaz:
http://arxiv.org/abs/2312.01196
Existing neural field representations for 3D object reconstruction either (1) utilize object-level representations, but suffer from low-quality details due to conditioning on a global latent code, or (2) are able to perfectly reconstruct the observat
Externí odkaz:
http://arxiv.org/abs/2309.03809
Data in Knowledge Graphs often represents part of the current state of the real world. Thus, to stay up-to-date the graph data needs to be updated frequently. To utilize information from Knowledge Graphs, many state-of-the-art machine learning approa
Externí odkaz:
http://arxiv.org/abs/2109.10896