# Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding

arXiv: https://arxiv.org/abs/2301.12165 · HTML: https://mlsanity.com/paper/2301.12165/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 28 Jan 2023.

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

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Multiscale Point Cloud Geometry Compression](https://mlsanity.com/paper/2011.03799/) (2020)
- [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)
- [Learned Point Cloud Geometry Compression](https://mlsanity.com/paper/1909.12037/) (2019)
- [Inter-Frame Compression for Dynamic Point Cloud Geometry Coding](https://mlsanity.com/paper/2207.12554/) (2022)
- [D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction](https://mlsanity.com/paper/2205.01135/) (2022)
- [Graph-based compression of dynamic 3D point cloud sequences](https://mlsanity.com/paper/1506.06096/) (2015)
- [Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention](https://mlsanity.com/paper/2208.12573/) (2022)

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

- [RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2503.12382/) (2025)
- [Learned Compression of Point Cloud Geometry and Attributes in a Single Model through Multimodal Rate-Control](https://mlsanity.com/paper/2408.00599/) (2024)
- [U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation](https://mlsanity.com/paper/2411.14501/) (2024)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation](https://mlsanity.com/paper/2602.00186/) (2026)

## Abstract (arXiv)

This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding. To this end, the reconstruction of the preceding Point Cloud Geometry (PCG) frame is progressively downscaled to generate multiscale temporal priors which are then scale-wise transferred and integrated with lower-scale spatial priors from the same frame to form the contextual information to improve occupancy probability approximation when processing the current PCG frame from one scale to another. Following the Common Test Conditions (CTC) defined in the standardization committee, the proposed method presents State-Of-The-Art (SOTA) compression performance, yielding 78% lossy BD-Rate gain to the latest standard-compliant V-PCC and 45% lossless bitrate reduction to the latest G-PCC. Even for recently-emerged learning-based solutions, our method still shows significant performance gains.