Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations
This addresses the problem of robust surface reconstruction for computer vision applications, though it appears incremental as it builds on existing neural implicit methods.
The paper tackles high-fidelity 3D reconstruction from depth images by introducing a curvature-guided sampling strategy that generates effective training data with minimal computational cost, achieving state-of-the-art results on synthetic and real-world datasets.
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 simple sampling strategy is proposed to generate highly effective training data, by incorporating differentiable geometric features computed directly based on the input depth images with only marginal computational cost. 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.