OctAttention: Octree-based Large-scale Contexts Model for Point Cloud Compression

• URL: http://arxiv.org/abs/2202.06028v1
• Date: Sat, 12 Feb 2022 10:06:12 GMT
• Title: OctAttention: Octree-based Large-scale Contexts Model for Point Cloud Compression
• Authors: Chunyang Fu, Ge Li, Rui Song, Wei Gao, Shan Liu
• Abstract summary: OctAttention employs the octree structure, a memory-efficient representation for point clouds. Our approach saves 95% coding time compared to the voxel-based baseline. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark.
• Score: 36.77271904751208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.

Related papers

• PVContext: Hybrid Context Model for Point Cloud Compression [61.24130634750288]
  We propose PVContext, a hybrid context model for effective octree-based point cloud compression. PVContext comprises two components with distinct modalities: the Voxel Context, which accurately represents local geometric information using voxels, and the Point Context, which efficiently preserves global shape information from point clouds.
  arXiv  Detail & Related papers  (2024-09-19T12:47:35Z)
• Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss [17.88391386335647]
  In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label. We introduce the context feature residuals into the context model to amplify the differences between contexts. We also add a multi-layer perception branch, that uses the mean squared error between its output and node occupancy as a loss function.
  arXiv  Detail & Related papers  (2024-07-11T14:08:37Z)
• GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute [51.4803148196217]
  We propose a graph-based quality enhancement network (GQE-Net) to reduce color distortion in point clouds. GQE-Net uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Experimental results show that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-03-24T02:33:45Z)
• ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression [6.509720419113212]
  We propose a sufficient yet efficient context model and design an efficient deep learning for point clouds. Specifically, we first propose a window-constrained multi-group coding strategy to exploit the autoregressive context. We also propose a dual transformer architecture to utilize the dependency of current node on its ancestors and siblings.
  arXiv  Detail & Related papers  (2022-11-20T09:20:32Z)
• Point Cloud Compression with Sibling Context and Surface Priors [47.96018990521301]
  We present a novel octree-based multi-level framework for large-scale point cloud compression. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree. We locally fit surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding.
  arXiv  Detail & Related papers  (2022-05-02T09:13:26Z)
• PointAttN: You Only Need Attention for Point Cloud Completion [89.88766317412052]
  Point cloud completion refers to completing 3D shapes from partial 3D point clouds. We propose a novel neural network for processing point cloud in a per-point manner to eliminate kNNs. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes.
  arXiv  Detail & Related papers  (2022-03-16T09:20:01Z)
• CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning [53.1436669083784]
  We propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud. For classification, we get a competitive result with the fully-supervised methods on ModelNet40 (92.5% accuracy) and ScanObjectNN (87.9% accuracy)
  arXiv  Detail & Related papers  (2022-01-20T15:04:12Z)
• DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration [12.471564670462344]
  This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.
  arXiv  Detail & Related papers  (2020-07-22T08:20:57Z)
• TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations [20.318695890515613]
  We propose an autoencoder, TearingNet, which tackles the challenging task of representing point clouds using a fixed-length descriptor. Our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.
  arXiv  Detail & Related papers  (2020-06-17T22:42:43Z)

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.