Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression

• URL: http://arxiv.org/abs/2510.08512v1
• Date: Thu, 09 Oct 2025 17:45:09 GMT
• Title: Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression
• Authors: Nikolaos Stathoulopoulos, Christoforos Kanellakis, George Nikolakopoulos,
• Abstract summary: We propose a deep compression framework based on semantic scene graphs.<n>We show that the framework achieves state-of-the-art compression rates, reducing data size by up to 98%.<n>It supports downstream applications such as multi-robot pose graph optimization and map merging.
• Score: 18.40946383877556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient transmission of 3D point cloud data is critical for advanced perception in centralized and decentralized multi-agent robotic systems, especially nowadays with the growing reliance on edge and cloud-based processing. However, the large and complex nature of point clouds creates challenges under bandwidth constraints and intermittent connectivity, often degrading system performance. We propose a deep compression framework based on semantic scene graphs. The method decomposes point clouds into semantically coherent patches and encodes them into compact latent representations with semantic-aware encoders conditioned by Feature-wise Linear Modulation (FiLM). A folding-based decoder, guided by latent features and graph node attributes, enables structurally accurate reconstruction. Experiments on the SemanticKITTI and nuScenes datasets show that the framework achieves state-of-the-art compression rates, reducing data size by up to 98% while preserving both structural and semantic fidelity. In addition, it supports downstream applications such as multi-robot pose graph optimization and map merging, achieving trajectory accuracy and map alignment comparable to those obtained with raw LiDAR scans.

Related papers

• Stable Diffusion is a Natural Cross-Modal Decoder for Layered AI-generated Image Compression [7.643300240138419]
  We introduce a scalable cross-modal compression framework that incorporates multiple human-comprehensible modalities.<n>Our framework encodes images into a layered bitstream consisting of a semantic layer that delivers high-level semantic information.<n>Our method proficiently restores both semantic and visual details, competing against baseline approaches at extremely lows.
  arXiv  Detail & Related papers  (2024-12-17T15:01:35Z)
• Implicit Neural Compression of Point Clouds [58.45774938982386]
  NeRC$textbf3$ is a novel point cloud compression framework leveraging implicit neural representations to handle both geometry and attributes.<n>For dynamic point clouds, 4D-NeRC$textbf3$ demonstrates superior geometry compression compared to state-of-the-art G-PCC and V-PCC standards.
  arXiv  Detail & Related papers  (2024-12-11T03:22:00Z)
• DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds [54.96190721255167]
  Lossy compression relies on an autoencoder to transform a point cloud into latent points for storage.<n>We propose a diffusion-based framework guided by sparse priors that achieves high reconstruction quality, especially at lows.
  arXiv  Detail & Related papers  (2024-11-21T05:41:35Z)
• Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer [52.40992954884257]
  3D visualization techniques have fundamentally transformed how we interact with digital content.<n>Massive data size of point clouds presents significant challenges in data compression.<n>We propose an end-to-end deep learning framework that seamlessly integrates PCAC with differentiable rendering.
  arXiv  Detail & Related papers  (2024-11-12T16:12:51Z)
• 3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods [0.0]
  Storing and transmitting LiDAR point cloud data is essential for many AV applications. Due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. We propose a new 3D-to-2D transformation which allows compression algorithms to efficiently exploit spatial correlations.
  arXiv  Detail & Related papers  (2024-02-18T19:08:19Z)
• PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation [77.44144260601182]
  We explore high-resolution architectures for 3D point cloud segmentation. We propose a unified pipeline named PointHR, which includes a knn-based sequence operator for feature extraction and a differential resampling operator. To evaluate these architectures for dense point cloud analysis, we conduct thorough experiments using S3DIS and ScanNetV2 datasets.
  arXiv  Detail & Related papers  (2023-10-11T09:29:17Z)
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [61.30276576646909]
  We propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task.
  arXiv  Detail & Related papers  (2022-10-06T10:18:16Z)
• GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression [16.98171403698783]
  We propose a heterogeneous approach with deep learning for lossy point cloud geometry compression. Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud.
  arXiv  Detail & Related papers  (2022-09-09T17:09:02Z)
• IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression [11.410441760314564]
  We propose a set of significant improvements to patch-based point cloud compression. Experiments show that the improved patch-based autoencoder outperforms the state-of-the-art in terms of rate-distortion performance.
  arXiv  Detail & Related papers  (2022-08-04T08:12:35Z)
• SoftPool++: An Encoder-Decoder Network for Point Cloud Completion [93.54286830844134]
  We propose a novel convolutional operator for the task of point cloud completion. The proposed operator does not require any max-pooling or voxelization operation. We show that our approach achieves state-of-the-art performance in shape completion at low and high resolutions.
  arXiv  Detail & Related papers  (2022-05-08T15:31:36Z)
• Variable Rate Compression for Raw 3D Point Clouds [5.107705550575662]
  We propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. Our network is capable of explicitly processing point clouds and generating a compressed description.
  arXiv  Detail & Related papers  (2022-02-28T15:15:39Z)

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.