Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
This addresses the challenge of matching incomplete or noisy 3D scans for applications like reconstruction and recognition, representing an incremental improvement over existing descriptors.
The paper tackles the problem of 3D local shape description and partial shape matching in depth scans with nuisances like noise and occlusion, proposing a novel descriptor that supports direct shape matching without preprocessing and demonstrates robustness and effectiveness in object/scene reconstruction and 3D object recognition.
Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the scans, without requiring any preprocessing such as scan denoising or converting into a mesh. First, we construct the descriptor by voxelizing the local shape within a uniquely defined local reference frame and concatenating geometric centroid and point density features extracted from each voxel. Second, we compare two descriptors by employing only corresponding voxels that are both non-empty, thus supporting matching incomplete local shape such as those close to scan boundary. Third, we propose a descriptor saliency measure and compute it from a descriptor-graph to improve shape matching performance. We demonstrate the descriptor's robustness and effectiveness for shape matching by comparing it with three state-of-the-art descriptors, and applying it to object/scene reconstruction and 3D object recognition.