Pith. sign in

REVIEW 2 cited by

Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2301.12165 v1 pith:WFHSRPFC submitted 2023-01-28 cs.CV eess.IV

Jianqiang Wang , Dandan Ding , Hao Chen , Zhan Ma This is my paper
classification cs.CVeess.IV
keywords multiscalecloudcompressionframegeometrypointcodingconditional
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression

    cs.MM 2025-08 conditional novelty 6.0 of 10

    AdaDPCC uses multiple slimmable coding routes with a learned rate controller to compress dynamic point clouds, cutting average bitrate and coding time versus state-of-the-art methods.

  2. Point Cloud Compression and Objective Quality Assessment: A Survey

    cs.CV 2025-06 conditional novelty 2.0 of 10

    A survey of point cloud compression and objective quality assessment that benchmarks representative methods on standard datasets and distills design insights.

Pith tools