PointInfinity: Resolution-Invariant Point Diffusion Models

We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution poin...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 10050 - 10060
Hlavní autori:	Huang, Zixuan, Johnson, Justin, Debnath, Shoubhik, Rehg, James M., Wu, Chao-Yuan
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 16.06.2024
Predmet:	3D Diffusion Model 3D Generation 3D reconstruction 3D Vision Computer vision Deep Learning Noise reduction Point cloud compression Shape Three-dimensional displays Training Transformer cores
ISSN:	1063-6919
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