ShapeMorph: 3D Shape Completion via Blockwise Discrete Diffusion

Jiahui Li, Pourya Shamsolmoali, Yue Lu, Masoumeh Zareapoor; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2818-2827

We introduce ShapeMorph a diffusion-based method specifically designed for generating precise and diverse 3D shape completions. By integrating an irregular discrete representation with a novel blockwise discrete diffusion model ShapeMorph can produce multiple high-quality shape completions while maintaining fidelity to the input. In particular each 3D shape is encoded into a compact sequence of irregularly distributed discrete variables ensuring an accurate capture of the object's topological details. We then propose a blockwise discrete diffusion model to precisely learn the shape completion distribution based on various incompleteness. We also introduce a Flow transformer into our diffusion process serving as a denoising network to enhance the modeling adaptability and flexibility. ShapeMorph addresses common challenges in existing methods such as poor completion limited diversity and misalignment with the input. Results show that ShapeMorph outperforms state-of-the-art methods and effectively processes a variety of input types and levels of incompleteness.