Point cloud quality metrics for incremental image-based 3D reconstruction

Image-based 3D reconstruction is a powerful method for accurately reconstructing an object’s geometry and texture from images. A crucial factor for the accuracy and completeness of the resulting reconstructed model is the choice of poses for capturing images, which is called view planning. One possible view planning strategy uses an iterative feedback loop that switches between planning poses and an incremental reconstruction to autonomously digitize an object without prior knowledge. However, this approach requires identifying which parts of an object are “poorly reconstructed” and thus would benefit from being part of additional images. This work explores the use of point cloud quality metrics to provide this feedback by comprehensively comparing a set of existing and newly introduced metrics in terms of their time-dependent behavior, similarity, and their applicability to view planning. Among the newly proposed metrics this work introduces the Reconstruction Quality Feedback (RQF) , which shows a significantly improved performance in simulations when being used for view planning. The effectiveness of RQF is also demonstrated for real objects on an autonomous robotic 3D digitization system.