# Range Image-Based Implicit Neural Compression for LiDAR Point Clouds

arXiv: https://arxiv.org/abs/2504.17229 · HTML: https://mlsanity.com/paper/2504.17229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [HNeRV: A Hybrid Neural Representation for Videos](https://mlsanity.com/paper/2304.02633/) (2023)
- [COIN++: Neural Compression Across Modalities](https://mlsanity.com/paper/2201.12904/) (2022)
- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression](https://mlsanity.com/paper/2005.07178/) (2020)
- [HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation](https://mlsanity.com/paper/2306.09818/) (2023)
- [OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression](https://mlsanity.com/paper/2202.06028/) (2022)
- [NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling](https://mlsanity.com/paper/2212.14593/) (2022)
- [Meta-Learning Sparse Implicit Neural Representations](https://mlsanity.com/paper/2110.14678/) (2021)
- [FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos](https://mlsanity.com/paper/2212.12294/) (2022)
- [Towards Scalable Neural Representation for Diverse Videos](https://mlsanity.com/paper/2303.14124/) (2023)
- [PS-NeRV: Patch-wise Stylized Neural Representations for Videos](https://mlsanity.com/paper/2208.03742/) (2022)
- [Boosting Neural Representations for Videos with a Conditional Decoder](https://mlsanity.com/paper/2402.18152/) (2024)
- [DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes](https://mlsanity.com/paper/2403.15679/) (2024)
- [RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding](https://mlsanity.com/paper/2206.01738/) (2022)
- [R-PCC: A Baseline for Range Image-based Point Cloud Compression](https://mlsanity.com/paper/2109.07717/) (2021)
- [RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization](https://mlsanity.com/paper/2403.12401/) (2024)
- [BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression](https://mlsanity.com/paper/2303.04027/) (2023)
- [Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data](https://mlsanity.com/paper/2404.06715/) (2024)

## Built on by (most cited first, 1 of 1)

- [DepthTCM: High Efficient Depth Compression via Physics-aware Transformer-CNN Mixed Architecture](https://mlsanity.com/paper/2603.21233/) (2026)

## Abstract (arXiv)

This paper presents a novel scheme to efficiently compress Light Detection and Ranging~(LiDAR) point clouds, enabling high-precision 3D scene archives, and such archives pave the way for a detailed understanding of the corresponding 3D scenes. We focus on 2D range images~(RIs) as a lightweight format for representing 3D LiDAR observations. Although conventional image compression techniques can be adapted to improve compression efficiency for RIs, their practical performance is expected to be limited due to differences in bit precision and the distinct pixel value distribution characteristics between natural images and RIs. We propose a novel implicit neural representation~(INR)--based RI compression method that effectively handles floating-point valued pixels. The proposed method divides RIs into depth and mask images and compresses them using patch-wise and pixel-wise INR architectures with model pruning and quantization, respectively. Experiments on the KITTI dataset show that the proposed method outperforms existing image, point cloud, RI, and INR-based compression methods in terms of 3D reconstruction and detection quality at low bitrates and decoding latency.