# Real-Time LiDAR Point Cloud Compression and Transmission for Resource-constrained Robots

arXiv: https://arxiv.org/abs/2502.06123 · HTML: https://mlsanity.com/paper/2502.06123/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion](https://mlsanity.com/paper/2509.06023/) (2025)
- [Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Clouds](https://mlsanity.com/paper/2508.20466/) (2025)
- [NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentation](https://mlsanity.com/paper/2602.00542/) (2026)
- [Towards Practical Lossless Neural Compression for LiDAR Point Clouds](https://mlsanity.com/paper/2603.25260/) (2026)
- [ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding](https://mlsanity.com/paper/2510.17068/) (2025)

## Abstract (arXiv)

LiDARs are widely used in autonomous robots due to their ability to provide accurate environment structural information. However, the large size of point clouds poses challenges in terms of data storage and transmission. In this paper, we propose a novel point cloud compression and transmission framework for resource-constrained robotic applications, called RCPCC. We iteratively fit the surface of point clouds with a similar range value and eliminate redundancy through their spatial relationships. Then, we use Shape-adaptive DCT (SA-DCT) to transform the unfit points and reduce the data volume by quantizing the transformed coefficients. We design an adaptive bitrate control strategy based on QoE as the optimization goal to control the quality of the transmitted point cloud. Experiments show that our framework achieves compression rates of 40× to 80× while maintaining high accuracy for downstream applications. our method significantly outperforms other baselines in terms of accuracy when the compression rate exceeds 70×. Furthermore, in situations of reduced communication bandwidth, our adaptive bitrate control strategy demonstrates significant QoE improvements. The code will be available at https://github.com/HITSZ-NRSL/RCPCC.git.