BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics
This addresses the need for fast, high-quality edge detection in applications such as urban planning or autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the problem of extracting structural information like boundaries and edges from 3D point clouds, proposing a method that uses local neighborhood statistics as features for a classification network, resulting in outperforming state-of-the-art techniques in quality and processing time.
Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios requiring interactivity. We propose to utilize a novel set of features describing the local neighborhood on a per-point basis via first and second order statistics as input for a simple and compact classification network to distinguish between non-edge, sharp-edge, and boundary points in the given data. Leveraging this feature embedding enables our algorithm to outperform the state-of-the-art techniques in terms of quality and processing time.