Enhancing Surface Neural Implicits With Curvature-Guided Sampling and Uncertainty-Augmented Representations
MCML Authors
Daniel Cremers
Prof. Dr.
Director
Abstract
Daniel Cremers
Prof. Dr.
Director
Abstract
Neural implicit representations have become a popular choice for modeling surfaces due to their adaptability in resolution and support for complex topology. While previous works have achieved impressive reconstruction quality by training on ground truth point clouds or meshes, they often do not discuss the data acquisition and ignore the effect of input quality and sampling methods during reconstruction. In this paper, we introduce a method that directly digests depth images for the task of high-fidelity 3D reconstruction. To this end, a novel local geometry feature computation method is proposed such that a simple sampling strategy can be adopted to generate highly effective training data. Due to its simplicity, our sampling strategy can be easily incorporated into diverse popular methods, allowing their training process to be more stable and efficient. Despite its simplicity, our method outperforms a range of both classical and learning-based baselines and demonstrates state-of-the-art results in both synthetic and real-world datasets.
inproceedings
GCPR 2024
German Conference on Pattern Recognition. Munich, Germany, Oct 10-13, 2024.Authors
L. Sang • A. Saroha • M. Gao • D. CremersLinks
DOIResearch Area
BibTeXKey: SSG+24