DPCD: A Quality Assessment Database for Dynamic Point Clouds
- URL: http://arxiv.org/abs/2505.12431v1
- Date: Sun, 18 May 2025 14:03:21 GMT
- Title: DPCD: A Quality Assessment Database for Dynamic Point Clouds
- Authors: Yating Liu, Yujie Zhang, Qi Yang, Yiling Xu, Zhu Li, Ye-Kui Wang,
- Abstract summary: We introduce a large-scale DPCQA database, named DPCD, which includes 15 reference DPCs and distorted DPCs from seven types of lossy compression and noise distortion.<n>A subjective experiment is conducted to obtain Mean Opinion Scores (MOS) from 21 viewers for analysis.<n>The experiment results show that DPCQA is more challenge than that of static point cloud.
- Score: 29.53306531548785
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC). Unlike static point clouds, DPCs are capable of capturing temporal changes within objects or scenes, offering a more accurate simulation of the real world. While significant progress has been made in the quality assessment research of static point cloud, little study has been done on Dynamic Point Cloud Quality Assessment (DPCQA), which hinders the development of quality-oriented applications, such as interframe compression and transmission in practical scenarios. In this paper, we introduce a large-scale DPCQA database, named DPCD, which includes 15 reference DPCs and 525 distorted DPCs from seven types of lossy compression and noise distortion. By rendering these samples to Processed Video Sequences (PVS), a comprehensive subjective experiment is conducted to obtain Mean Opinion Scores (MOS) from 21 viewers for analysis. The characteristic of contents, impact of various distortions, and accuracy of MOSs are presented to validate the heterogeneity and reliability of the proposed database. Furthermore, we evaluate the performance of several objective metrics on DPCD. The experiment results show that DPCQA is more challenge than that of static point cloud. The DPCD, which serves as a catalyst for new research endeavors on DPCQA, is publicly available at https://huggingface.co/datasets/Olivialyt/DPCD.
Related papers
- Point Cloud Compression and Objective Quality Assessment: A Survey [22.27629022031786]
3D point cloud data is driven by applications in autonomous driving, robotics, and immersive environments.<n>Unlike traditional 2D media, point clouds present unique challenges due to their irregular structure, high data volume, and complex attributes.
arXiv Detail & Related papers (2025-06-28T14:34:24Z) - Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment [49.36799270585947]
No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference.
We propose a novel contrastive pre-training framework tailored for PCQA (CoPA)
Our method outperforms the state-of-the-art PCQA methods on popular benchmarks.
arXiv Detail & Related papers (2024-03-15T07:16:07Z) - Activating Frequency and ViT for 3D Point Cloud Quality Assessment
without Reference [0.49157446832511503]
We propose no-reference quality metric of a given 3D-PC.
To map the input attributes to quality score, we use a light-weight hybrid deep model; combined of Deformable Convolutional Network (DCN) and Vision Transformers (ViT)
The results show that our approach outperforms state-of-the-art NR-PCQA measures and even some FR-PCQA on PointXR.
arXiv Detail & Related papers (2023-12-10T19:13:34Z) - Geometry-Aware Video Quality Assessment for Dynamic Digital Human [56.17852258306602]
We propose a novel no-reference (NR) geometry-aware video quality assessment method for DDH-QA challenge.
The proposed method achieves state-of-the-art performance on the DDH-QA database.
arXiv Detail & Related papers (2023-10-24T16:34:03Z) - SJTU-TMQA: A quality assessment database for static mesh with texture
map [28.821971310570436]
We create a large-scale textured mesh quality assessment database, namely SJTU-TMQA, which includes 21 reference meshes and 945 distorted samples.
13 state-of-the-art objective metrics are evaluated on SJTU-TMQA. The results report the highest correlation of around 0.6, indicating the need for more effective objective metrics.
arXiv Detail & Related papers (2023-09-27T14:18:04Z) - Robustness and Generalization Performance of Deep Learning Models on
Cyber-Physical Systems: A Comparative Study [71.84852429039881]
Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise.
We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
arXiv Detail & Related papers (2023-06-13T12:43:59Z) - DDH-QA: A Dynamic Digital Humans Quality Assessment Database [55.69700918818879]
We construct a large-scale dynamic digital human quality assessment database with diverse motion content as well as multiple distortions.
Ten types of common motion are employed to drive the DDHs and a total of 800 DDHs are generated in the end.
arXiv Detail & Related papers (2022-12-24T13:35:31Z) - TCDM: Transformational Complexity Based Distortion Metric for Perceptual
Point Cloud Quality Assessment [24.936061591860838]
The goal of objective point cloud quality assessment (PCQA) research is to develop metrics that measure point cloud quality in a consistent manner.
We evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference.
The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases.
arXiv Detail & Related papers (2022-10-10T13:20:51Z) - Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric [58.309735075960745]
This paper explores the way of dealing with point cloud quality assessment (PCQA) tasks via video quality assessment (VQA) methods.
We generate the captured videos by rotating the camera around the point clouds through several circular pathways.
We extract both spatial and temporal quality-aware features from the selected key frames and the video clips through using trainable 2D-CNN and pre-trained 3D-CNN models.
arXiv Detail & Related papers (2022-08-30T08:59:41Z) - Reduced Reference Perceptual Quality Model and Application to Rate
Control for 3D Point Cloud Compression [61.110938359555895]
In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate.
We propose a linear perceptual quality model whose variables are the V-PCC geometry and color quantization parameters.
Subjective quality tests with 400 compressed 3D point clouds show that the proposed model correlates well with the mean opinion score.
We show that for the same target bit rate, ratedistortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.
arXiv Detail & Related papers (2020-11-25T12:42:02Z)
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.