Multi-view Point Cloud Registration with Adaptive Convergence Threshold and its Application on 3D Model Retrieval
This work addresses 3D model reconstruction and retrieval for multimedia and AI applications, but appears incremental as it builds on existing ICP and motion averaging methods.
The paper tackles multi-view point cloud registration by proposing a framework that combines ICP with motion averaging and uses an adaptive convergence threshold, then applies it to 3D model retrieval by discriminating face and non-face models using geometric saliency maps. The experiments demonstrate the framework's effectiveness.
Multi-view point cloud registration is a hot topic in the communities of multimedia technology and artificial intelligence (AI). In this paper, we propose a framework to reconstruct the 3D models by the multi-view point cloud registration algorithm with adaptive convergence threshold, and subsequently apply it to 3D model retrieval. The iterative closest point (ICP) algorithm is implemented combining with the motion average algorithm for the registration of multi-view point clouds. After the registration process, we design applications for 3D model retrieval. The geometric saliency map is computed based on the vertex curvature. The test facial triangle is then generated based on the saliency map, which is applied to compare with the standard facial triangle. The face and non-face models are then discriminated. The experiments and comparisons prove the effectiveness of the proposed framework.