Fast and Accurate Normal Estimation by Efficient 3d Edge Detection

Accurate surface normal computation is one of the most basic and important tasks for 3d perception. While much progress has been made in speeding up normal estimation algorithms and improving their accuracy, a significant inaccuracy still remains even with modern implementations, which is the correct determination of surface normals close to non-differentiable surface edges. Current algorithms tend to amalgamate neighborhood points from independent surfaces yielding normals that neither fit well to the one nor the other surface. This paper introduces a fast and accurate 3d edge detection algorithm suitable to detect discontinuities both in depth and on surfaces with nearly 90% accuracy at rates beyond 30 Hz. Based on this method, we demonstrate how established normal estimation algorithms can be extended for edge-awareness. Additionally, a new edge-aware, fast, accurate, and robust normal estimation approach is described which exploits the data structures computed for 3d edge detection and estimates normals at 23 Hz. We assess the performance of all proposed methods and compare them with other state-ofthe-art approaches.