Point Cloud Compression via Constrained Optimal Transport

• URL: http://arxiv.org/abs/2403.08236v1
• Date: Wed, 13 Mar 2024 04:36:24 GMT
• Title: Point Cloud Compression via Constrained Optimal Transport
• Authors: Zezeng Li, Weimin Wang, Ziliang Wang, Na Lei
• Abstract summary: COT-PCC takes compressed features as an extra constraint of optimal transport. It learns the distribution transformation between original and reconstructed points. COT-PCC outperforms state-of-the-art methods in terms of both CD and PSNR metrics.
• Score: 10.795619052889952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel point cloud compression method COT-PCC by formulating the task as a constrained optimal transport (COT) problem. COT-PCC takes the bitrate of compressed features as an extra constraint of optimal transport (OT) which learns the distribution transformation between original and reconstructed points. Specifically, the formulated COT is implemented with a generative adversarial network (GAN) and a bitrate loss for training. The discriminator measures the Wasserstein distance between input and reconstructed points, and a generator calculates the optimal mapping between distributions of input and reconstructed point cloud. Moreover, we introduce a learnable sampling module for downsampling in the compression procedure. Extensive results on both sparse and dense point cloud datasets demonstrate that COT-PCC outperforms state-of-the-art methods in terms of both CD and PSNR metrics. Source codes are available at \url{https://github.com/cognaclee/PCC-COT}.

Related papers

• Point Cloud Compression with Bits-back Coding [32.9521748764196]
  This paper specializes in using a deep learning-based probabilistic model to estimate the Shannon's entropy of the point cloud information. Once the entropy of the point cloud dataset is estimated, we use the learned CVAE model to compress the geometric attributes of the point clouds. The novelty of our method with bits-back coding specializes in utilizing the learned latent variable model of the CVAE to compress the point cloud data.
  arXiv  Detail & Related papers  (2024-10-09T06:34:48Z)
• Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression [28.316347464011056]
  PoLoPCAC is an efficient and generic PCAC method that achieves high compression efficiency and strong generalizability simultaneously. Our method can be instantly deployed once trained on a Synthetic 2k-ShapeNet dataset. Experiments show that our method can enjoy continuous bit-rate reduction over the latest G-PCCv23 on various datasets.
  arXiv  Detail & Related papers  (2024-04-10T11:40:02Z)
• Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression [39.052583172727324]
  The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds. This paper proposes a hierarchical prior-based super resolution method for point cloud geometry compression.
  arXiv  Detail & Related papers  (2024-02-17T11:15:38Z)
• CARNet:Compression Artifact Reduction for Point Cloud Attribute [37.78660069355263]
  A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce compression artifacts. The proposed method first generates multiple Most-Probable Sample Offsets (MPSOs) as potential compression distortion approximations, and then linearly weights them for artifact mitigation.
  arXiv  Detail & Related papers  (2022-09-17T08:05:35Z)
• Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention [25.054578678654796]
  This work suggests the neighborhood point attention (NPA) to tackle them. We first use k nearest neighbors (kNN) to construct adaptive local neighborhood. We then leverage the self-attention mechanism to dynamically aggregate information within this neighborhood.
  arXiv  Detail & Related papers  (2022-08-26T10:44:30Z)
• Density-preserving Deep Point Cloud Compression [72.0703956923403]
  We propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features.
  arXiv  Detail & Related papers  (2022-04-27T03:42:15Z)
• An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems [77.88178159830905]
  Sparsity-Inducing Distribution-based Compression (SIDCo) is a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) Our evaluation shows SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.
  arXiv  Detail & Related papers  (2021-01-26T13:06:00Z)
• MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models [78.93424358827528]
  We present a novel compression algorithm for reducing the storage streams of LiDAR sensor data. Our method significantly reduces the joint geometry and intensity over prior state-of-the-art LiDAR compression methods.
  arXiv  Detail & Related papers  (2020-11-15T17:41:14Z)
• PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning [62.440827696638664]
  We introduce a simple algorithm that directly compresses the model differences between neighboring workers. Inspired by the PowerSGD for centralized deep learning, this algorithm uses power steps to maximize the information transferred per bit.
  arXiv  Detail & Related papers  (2020-08-04T09:14:52Z)
• OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression [77.8842824702423]
  We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the memory footprint. Our algorithm can be used to reduce the onboard and offboard storage of LiDAR points for applications such as self-driving cars.
  arXiv  Detail & Related papers  (2020-05-14T17:48:49Z)
• Optimizing Vessel Trajectory Compression [71.42030830910227]
  In previous work we introduced a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. This methodology can provide reliable trajectory synopses with little deviations from the original course by discarding at least 70% of the raw data as redundant. However, such trajectory compression is very sensitive to parametrization. We take into account the type of each vessel in order to provide a suitable configuration that can yield improved trajectory synopses.
  arXiv  Detail & Related papers  (2020-05-11T20:38:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.