SR-CurvANN: Advancing 3D Surface Reconstruction through Curvature-Aware Neural Networks

TL;DR

SR-CurvANN introduces a neural network-based method that leverages curvature information for realistic 3D surface reconstruction, effectively filling missing data with high accuracy and detail.

Contribution

The paper presents a novel curvature-aware neural network approach for 3D surface reconstruction that outperforms existing methods in realism and precision.

Findings

Successfully reconstructs 3D surfaces with complex details

Achieves high realism in hole filling across diverse models

Demonstrates superior performance on 959 models

Abstract

Incomplete or missing data in three-dimensional (3D) models can lead to erroneous or flawed renderings, limiting their usefulness in applications such as visualization, geometric computation, and 3D printing. Conventional surface-repair techniques often fail to infer complex geometric details in missing areas. Neural networks successfully address hole-filling tasks in 2D images using inpainting techniques. The combination of surface reconstruction algorithms, guided by the model's curvature properties and the creativity of neural networks in the inpainting processes should provide realistic results in the hole completion task. In this paper, we propose a novel method entitled SR-CurvANN (Surface Reconstruction Based on Curvature-Aware Neural Networks) that incorporates neural network-based 2D inpainting to effectively reconstruct 3D surfaces. We train the neural networks with images that represent planar representations of the curvature at vertices of hundreds of 3D models. Once the missing areas have been inferred, a coarse-to-fine surface deformation process ensures that the surface fits the reconstructed curvature image. Our proposal makes it possible to learn and generalize patterns from a wide variety of training 3D models, generating comprehensive inpainted curvature images and surfaces. Experiments conducted on 959 models with several holes have demonstrated that SR-CurvANN excels in the shape completion process, filling holes with a remarkable level of realism and precision.