MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step
This addresses surface reconstruction for 3D modeling, but it is incremental as it builds on existing overfitting-based methods with multi-scale optimization.
The paper tackles the problem of reconstructing continuous surfaces from raw 3D point clouds, where neural networks often smooth local details, and proposes MultiPull to learn multi-scale implicit fields, achieving state-of-the-art performance on object and scene benchmarks.
Reconstructing a continuous surface from a raw 3D point cloud is a challenging task. Recent methods usually train neural networks to overfit on single point clouds to infer signed distance functions (SDFs). However, neural networks tend to smooth local details due to the lack of ground truth signed distances or normals, which limits the performance of overfitting-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 by optimizing 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.