SDA-SNE: Spatial Discontinuity-Aware Surface Normal Estimation via Multi-Directional Dynamic Programming

TL;DR

SDA-SNE introduces a spatial discontinuity-aware surface normal estimation method that adaptively refines depth gradients using multi-directional dynamic programming and recursive polynomial interpolation, significantly improving accuracy near edges and ridges.

Contribution

The paper proposes a novel multi-directional dynamic programming strategy and recursive polynomial interpolation for enhanced surface normal estimation, especially at spatial discontinuities.

Findings

Outperforms state-of-the-art methods in accuracy near discontinuities

Converges rapidly within a few iterations for efficiency

Demonstrates robustness against noise and environmental variations

Abstract

The state-of-the-art (SoTA) surface normal estimators (SNEs) generally translate depth images into surface normal maps in an end-to-end fashion. Although such SNEs have greatly minimized the trade-off between efficiency and accuracy, their performance on spatial discontinuities, e.g., edges and ridges, is still unsatisfactory. To address this issue, this paper first introduces a novel multi-directional dynamic programming strategy to adaptively determine inliers (co-planar 3D points) by minimizing a (path) smoothness energy. The depth gradients can then be refined iteratively using a novel recursive polynomial interpolation algorithm, which helps yield more reasonable surface normals. Our introduced spatial discontinuity-aware (SDA) depth gradient refinement strategy is compatible with any depth-to-normal SNEs. Our proposed SDA-SNE achieves much greater performance than all other SoTA approaches, especially near/on spatial discontinuities. We further evaluate the performance of SDA-SNE with respect to different iterations, and the results suggest that it converges fast after only a few iterations. This ensures its high efficiency in various robotics and computer vision applications requiring real-time performance. Additional experiments on the datasets with different extents of random noise further validate our SDA-SNE's robustness and environmental adaptability. Our source code, demo video, and supplementary material are publicly available at mias.group/SDA-SNE.