# RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds

arXiv: https://arxiv.org/abs/2503.12382 · HTML: https://mlsanity.com/paper/2503.12382/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression](https://mlsanity.com/paper/2005.07178/) (2020)
- [Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression](https://mlsanity.com/paper/2111.10633/) (2021)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs](https://mlsanity.com/paper/2311.12862/) (2023)
- [MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation](https://mlsanity.com/paper/2407.11682/) (2024)
- [Efficient 3D Deep LiDAR Odometry](https://mlsanity.com/paper/2111.02135/) (2021)
- [Inter-Frame Compression for Dynamic Point Cloud Geometry Coding](https://mlsanity.com/paper/2207.12554/) (2022)
- [RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding](https://mlsanity.com/paper/2206.01738/) (2022)
- [Real-Time Spatio-Temporal LiDAR Point Cloud Compression](https://mlsanity.com/paper/2008.06972/) (2020)
- [SCP: Spherical-Coordinate-based Learned Point Cloud Compression](https://mlsanity.com/paper/2308.12535/) (2023)
- [R-PCC: A Baseline for Range Image-based Point Cloud Compression](https://mlsanity.com/paper/2109.07717/) (2021)
- [Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression](https://mlsanity.com/paper/2209.12512/) (2022)
- [ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression](https://mlsanity.com/paper/2211.10916/) (2022)
- [3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation](https://mlsanity.com/paper/2405.03388/) (2024)
- [Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention](https://mlsanity.com/paper/2208.12573/) (2022)
- [RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction](https://mlsanity.com/paper/2402.02192/) (2024)
- [Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding](https://mlsanity.com/paper/2301.12165/) (2023)
- [Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned Geometry Codec for Large-Scale Point Cloud Scenes](https://mlsanity.com/paper/2404.13550/) (2024)
- [FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI](https://mlsanity.com/paper/2307.15005/) (2023)
- [Coarse-to-fine Hybrid 3D Mapping System with Co-calibrated Omnidirectional Camera and Non-repetitive LiDAR](https://mlsanity.com/paper/2301.12934/) (2023)

## Built on by (most cited first, 12 of 12)

- [Reflectance Prediction-based Knowledge Distillation for Robust 3D Object Detection in Compressed Point Clouds](https://mlsanity.com/paper/2505.17442/) (2025)
- [Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Clouds](https://mlsanity.com/paper/2508.20466/) (2025)
- [Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression](https://mlsanity.com/paper/2510.08512/) (2025)
- [AnyPcc: Compressing Any Point Cloud with a Single Universal Model](https://mlsanity.com/paper/2510.20331/) (2025)
- [Foundry: Distilling 3D Foundation Models for the Edge](https://mlsanity.com/paper/2511.20721/) (2025)
- [Towards Practical Lossless Neural Compression for LiDAR Point Clouds](https://mlsanity.com/paper/2603.25260/) (2026)
- [Been There, Scanned That: Nostalgia-Driven LiDAR Compression for Self-Driving Cars](https://mlsanity.com/paper/2511.00652/) (2025)
- [ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders](https://mlsanity.com/paper/2511.14070/) (2025)
- [FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds](https://mlsanity.com/paper/2511.20065/) (2025)
- [Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression](https://mlsanity.com/paper/2605.18006/) (2026)
- [Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression](https://mlsanity.com/paper/2608.00687/) (2026)
- [Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives](https://mlsanity.com/paper/2608.09541/) (2026)

## Abstract (arXiv)

Despite the substantial advancements demonstrated by learning-based neural models in the LiDAR Point Cloud Compression (LPCC) task, realizing real-time compression - an indispensable criterion for numerous industrial applications - remains a formidable challenge. This paper proposes RENO, the first real-time neural codec for 3D LiDAR point clouds, achieving superior performance with a lightweight model. RENO skips the octree construction and directly builds upon the multiscale sparse tensor representation. Instead of the multi-stage inferring, RENO devises sparse occupancy codes, which exploit cross-scale correlation and derive voxels' occupancy in a one-shot manner, greatly saving processing time. Experimental results demonstrate that the proposed RENO achieves real-time coding speed, 10 fps at 14-bit depth on a desktop platform (e.g., one RTX 3090 GPU) for both encoding and decoding processes, while providing 12.25% and 48.34% bit-rate savings compared to G-PCCv23 and Draco, respectively, at a similar quality. RENO model size is merely 1MB, making it attractive for practical applications. The source code is available at https://github.com/NJUVISION/RENO.