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CVPR2024Top-tier venue

PointInfinity: Resolution-Invariant Point Diffusion Models

Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg, Chao-Yuan Wu

2024Year
10Top-tier citations

Abstract

1k 4k 16k 131k Surface Image Figure 1. We present a resolution-invariant point cloud diffusion model that trains at low-resolution (down to 64 points), but generates high-resolution point clouds (up to 131k points). This test-time resolution scaling improves our generation quality. We visualize our high-resolution 131k point clouds by converting them to a continuous surface.

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