Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps - Moradi, Saed; Sepanj, Hadi; Memarmoghadam, Alireza; Laurendeau, Denis; Fieguth, Paul (Journal of Computational Vision and Imaging Systems, 2024)
In this paper, we introduce a fast and precise surface normal estimation technique designed explicitly for depth maps, also known as organized point clouds. Our approach formulates the surface normal estimation as a closed-form expression, effectively mitigating the impact of measurement noise through multi-directional averaging. We then streamline the multi-directional normal estimation process for efficiency. Additionally, we propose a straightforward yet powerful method to eliminate inaccurate normal estimations at depth discontinuities, making our approach object boundary-aware. Comparative analyses with established surface normal estimation algorithms reveal that our method not only excels in accuracy but also exhibits the speed required for real-time applications.