Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds
This work addresses shape reconstruction for computer graphics and vision applications, offering an incremental improvement by enhancing efficiency and quality over traditional deep learning approaches.
The paper tackles the problem of reconstructing 3D shapes from point clouds by introducing Neural Poisson Surface Reconstruction (nPSR), which uses Fourier Neural Operators to solve the Poisson equation, achieving superior reconstruction quality, faster running times, and resolution-agnostic performance compared to existing methods.
We introduce Neural Poisson Surface Reconstruction (nPSR), an architecture for shape reconstruction that addresses the challenge of recovering 3D shapes from points. Traditional deep neural networks face challenges with common 3D shape discretization techniques due to their computational complexity at higher resolutions. To overcome this, we leverage Fourier Neural Operators to solve the Poisson equation and reconstruct a mesh from oriented point cloud measurements. nPSR exhibits two main advantages: First, it enables efficient training on low-resolution data while achieving comparable performance at high-resolution evaluation, thanks to the resolution-agnostic nature of FNOs. This feature allows for one-shot super-resolution. Second, our method surpasses existing approaches in reconstruction quality while being differentiable and robust with respect to point sampling rates. Overall, the neural Poisson surface reconstruction not only improves upon the limitations of classical deep neural networks in shape reconstruction but also achieves superior results in terms of reconstruction quality, running time, and resolution agnosticism.