Towards Robust Retrieval for Imperfectly Scanned Point Cloud Objects

Justin Lev, Joo Hwee Lim, Nizar Ouarti, Mounir Mokhtari

Abstract: With the development of 3D model analysis and particularly on classification challenge, the algorithms are getting better and better. Since no large dataset of scanned models is available, evaluating the algorithms in real-life scenarios is not straightforward. For now, these studies rely on ModelNet, a dataset of CAD models. Moreover, no studies considered the robustness to common recording data corruptions like occlusion or noise. In this paper, we present a preliminary study to assess the retrieval performances of point cloud-based algorithms for occluded or noisy objects. The experiment shows very promising results, even in the case of deep learning networks which has been pre-trained with CAD models. Indeed, more than 90% of the objects are retrieved when even only 40% of the query object is visible.