# Activating Frequency and ViT for 3D Point Cloud Quality Assessment without Reference

arXiv: https://arxiv.org/abs/2312.05972 · HTML: https://mlsanity.com/paper/2312.05972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://mlsanity.com/paper/2106.04803/) (2021)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network](https://mlsanity.com/paper/2210.16478/) (2022)
- [BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios](https://mlsanity.com/paper/2302.04796/) (2023)
- [End-to-end deep multi-score model for No-reference stereoscopic image quality assessment](https://mlsanity.com/paper/2211.01374/) (2022)

## Abstract (arXiv)

Deep learning-based quality assessments have significantly enhanced perceptual multimedia quality assessment, however it is still in the early stages for 3D visual data such as 3D point clouds (PCs). Due to the high volume of 3D-PCs, such quantities are frequently compressed for transmission and viewing, which may affect perceived quality. Therefore, we propose no-reference quality metric of a given 3D-PC. Comparing to existing methods that mostly focus on geometry or color aspects, we propose integrating frequency magnitudes as indicator of spatial degradation patterns caused by the compression. 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). Experiments are carried out on ICIP20 [1], PointXR [2] dataset, and a new big dataset called BASICS [3]. The results show that our approach outperforms state-of-the-art NR-PCQA measures and even some FR-PCQA on PointXR. The implementation code can be found at: https://github.com/o-messai/3D-PCQA