PointPCA+: Extending PointPCA objective quality assessment metric

• URL: http://arxiv.org/abs/2311.13880v1
• Date: Thu, 23 Nov 2023 10:05:31 GMT
• Title: PointPCA+: Extending PointPCA objective quality assessment metric
• Authors: Xuemei Zhou, Evangelos Alexiou, Irene Viola, Pablo Cesar
• Abstract summary: PointPCA+ is a set of perceptually-relevant descriptors for Point Cloud Quality Assessment (PCQA) metric. PointPCA+ employs PCA only on the geometry data while enriching existing geometry and texture descriptors, that are computed more efficiently. Tests show that PointPCA+ achieves predictive high performance against subjective ground truth scores obtained from publicly available datasets.
• Score: 4.674509064536047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A computationally-simplified and descriptor-richer Point Cloud Quality Assessment (PCQA) metric, namely PointPCA+, is proposed in this paper, which is an extension of PointPCA. PointPCA proposed a set of perceptually-relevant descriptors based on PCA decomposition that were applied to both the geometry and texture data of point clouds for full reference PCQA. PointPCA+ employs PCA only on the geometry data while enriching existing geometry and texture descriptors, that are computed more efficiently. Similarly to PointPCA, a total quality score is obtained through a learning-based fusion of individual predictions from geometry and texture descriptors that capture local shape and appearance properties, respectively. Before feature fusion, a feature selection module is introduced to choose the most effective features from a proposed super-set. Experimental results show that PointPCA+ achieves high predictive performance against subjective ground truth scores obtained from publicly available datasets. The code is available at \url{https://github.com/cwi-dis/pointpca_suite/}.

Related papers

• LoGDesc: Local geometric features aggregation for robust point cloud registration [4.888434990566421]
  This paper introduces a new hybrid descriptor for 3D point matching and point cloud registration. It combines local geometrical properties and learning-based feature propagation for each point's neighborhood structure description.
  arXiv  Detail & Related papers  (2024-10-03T12:11:22Z)
• KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation [87.23575166061413]
  KP-RED is a unified KeyPoint-driven REtrieval and Deformation framework. It takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models.
  arXiv  Detail & Related papers  (2024-03-15T08:44:56Z)
• Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysis [59.12922158979068]
  Recently-based methods have shown strong performance in point cloud analysis. Simple architectures are able to learn geometric features in local point groups yet fail to model long-range dependencies directly. We propose Point Deformable Network (PDNet) to capture long-range relations with strong representation ability.
  arXiv  Detail & Related papers  (2023-12-20T14:52:07Z)
• No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction [19.128878108831287]
  We propose a no-reference point cloud quality assessment (NR-PCQA) method with local area correlation analysis capability, denoted as COPP-Net. More specifically, we split a point cloud into patches, generate texture and structure features for each patch, and fuse them into patch features to predict patch quality. Experimental results show that our method outperforms the state-of-the-art benchmark NR-PCQA methods.
  arXiv  Detail & Related papers  (2023-05-13T03:20:33Z)
• Point Cloud Quality Assessment using 3D Saliency Maps [37.290843791053256]
  We propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction. Specifically, we first propose a projection-based point cloud saliency map generation method, in which depth information is introduced to better reflect the geometric characteristics of point clouds. Finally, a saliency-based pooling strategy is proposed to generate the final quality score.
  arXiv  Detail & Related papers  (2022-09-30T13:59:09Z)
• Object Localization under Single Coarse Point Supervision [107.46800858130658]
  We propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL) In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision.
  arXiv  Detail & Related papers  (2022-03-17T14:14:11Z)
• PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths [60.32185890237936]
  We design a novel neural network, named PMP-Net++, to mimic behavior of an earth mover. It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape.
  arXiv  Detail & Related papers  (2022-02-19T03:00:40Z)
• PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation [50.3020332934185]
  An unsupervised point cloud object retrieval and pose estimation method, called PCRP, is proposed in this work. Experiments on the ModelNet40 dataset demonstrate the superior performance of PCRP in comparison with traditional and learning based methods.
  arXiv  Detail & Related papers  (2022-02-16T03:37:43Z)
• FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis [12.91948651812873]
  Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. This paper proposes a distributed PCA algorithm called FAST-PCA (Fast and exAct diSTributed PCA)
  arXiv  Detail & Related papers  (2021-08-27T16:10:59Z)
• Skeleton-bridged Point Completion: From Global Inference to Local Adjustment [48.2757171993437]
  We propose a skeleton-bridged point completion network (SK-PCN) for shape completion. Given a partial scan, our method first predicts its 3D skeleton to obtain the global structure. We decouple the shape completion into structure estimation and surface reconstruction, which eases the learning difficulty.
  arXiv  Detail & Related papers  (2020-10-14T22:49:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.