Geometry quality assessment (GQA) of colorless point clouds is crucial for evaluating the performance of emerging point cloud-based solutions (e.g., watermarking, compression, and 3-Dimensional (3D) reconstruction). Unfortunately, existing objective GQA approaches are traditional full-reference metrics, whereas state-of-the-art learning-based point cloud quality assessment (PCQA) methods target both color and geometry distortions, neither of which are qualified for the no-reference GQA task. In addition, the lack of large-scale GQA datasets with subjective scores, which are always imprecise, biased, and inconsistent, also hinders the development of learning-based GQA metrics. Driven by these limitations, this paper proposes a no-reference geometry-only quality assessment approach based on list-wise rank learning, termed LRL-GQA, which comprises of a geometry quality assessment network (GQANet) and a list-wise rank learning network (LRLNet). The proposed LRL-GQA formulates the no-reference GQA as a list-wise rank problem, with the objective of directly optimizing the entire quality ordering. Specifically, a large dataset containing a variety of geometry-only distortions is constructed first, named LRL dataset, in which each sample is label-free but coupled with quality ranking information. Then, the GQANet is designed to capture intrinsic multi-scale patch-wise geometric features in order to predict a quality index for each point cloud. After that, the LRLNet leverages the LRL dataset and a likelihood loss to train the GQANet and ranks the input list of degraded point clouds according to their distortion levels. In addition, the pre-trained GQANet can be fine-tuned further to obtain absolute quality scores. Experimental results demonstrate the superior performance of the proposed no-reference LRL-GQA method compared with existing full-reference GQA metrics.

Objective geometry quality assessment of point clouds is essential to evaluate the performance of a wide range of point cloud-based solutions, such as denoising, simplification, reconstruction, and watermarking. Existing point cloud quality assessment (PCQA) methods dedicate to assigning absolute quality scores to distorted point clouds. Their performance is strongly reliant on the quality and quantity of subjective ground-truth scores for training, which are challenging to gather and have been shown to be imprecise, biased, and inconsistent. Furthermore, the majority of existing objective geometry quality assessment approaches are carried out by full-reference traditional metrics. So far, point-based no-reference geometry-only quality assessment techniques have not yet been investigated. This paper presents PRL-GQA, the first pairwise learning framework for no-reference geometry-only quality assessment of point clouds, to the best of our knowledge. The proposed PRL-GQA framework employs a siamese deep architecture, which takes as input a pair of point clouds and outputs their rank order. Each siamese architecture branch is a geometry quality assessment network (GQANet), which is designed to extract multi-scale quality-aware geometric features and output a quality index for the input point cloud. Then, based on the predicted quality indexes, a pairwise rank learning module is introduced to rank the relative quality of a pair of degraded point clouds.Extensive experiments demonstrate the effectiveness of the proposed PRL-GQA framework. Furthermore, the results also show that the fine-tuned no-reference GQANet performs competitively when compared to existing full-reference geometry quality assessment metrics.

Point cloud denoising is a crucial and fundamental step in geometry processing, which has achieved significant progress in the last two decades. Denoising real-world noisy point clouds is a very challenging problem since it is hard to describe the complex real-world noise by simple distributions such as Gaussian distribution. Furthermore, existing methods may suffer from performance degradation when dealing with real-world noisy point clouds with complex structures, which contain not only sharp features (sharp edges, sharp corners, etc.) but also smooth features, fine features, etc. To solve the above-mentioned problems, we propose a novel structure-aware denoising approach by exploiting the prior information in both external clean point clouds and the given noisy point cloud. We first group nonlocal self-similarity (NSS) patches from a set of external clean point clouds. Then, we employ the Gaussian Mixture Model (GMM) learning algorithm to learn external NSS priors over patch groups. Next, the internal priors are learned from the given noisy point cloud in the same way to refine the prior model. We integrate both the learned external and internal priors into a set of orthogonal dictionaries to efficiently estimate point normals. Finally, we propose a feature-aware point updating method through adaptive neighborhood selection to reposition points to match the estimated normals. Extensive experiments show that our approach achieves favorable comprehensive performance compared with many popular or state-of-the-art methods in terms of both objective and visual perception. The source code can be found at https://zhiyongsu.github.io.