@inproceedings{
noda2024multipull,
title={MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step},
author={Takeshi Noda and Chao Chen and Weiqi Zhang and Xinhai Liu and Yu-Shen Liu and Zhizhong Han},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=XxE8mL1bCO}
}
Reconstructing a continuous surface from a raw 3D point cloud is a challenging task. Latest methods employ supervised learning or pretrained priors to learn a signed distance function (SDF). However, neural networks tend to smooth local details due to the lack of ground truth signed distnaces or normals, which limits the performance of learning-based methods in reconstruction tasks. To resolve this issue, we propose a novel method, named MultiPull, to learn multi-scale implicit fields from raw point clouds to optimize accurate SDFs from coarse to fine. We achieve this by mapping 3D query points into a set of frequency features, which makes it possible to leverage multi-level features during optimization. Meanwhile, we introduce optimization constraints from the perspective of spatial distance and normal consistency, which play a key role in point cloud reconstruction based on multi-scale optimization strategies. Our experiments on widely used object and scene benchmarks demonstrate that our method outperforms the state-of-the-art methods in surface reconstruction.
MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step
Takeshi Noda Chao Chen Weiqi Zhang Xinhai Liu Yu-Shen Liu Zhizhong Han