Octree-based Point-Cloud Compression

@inproceedings{Schnabel2006OctreebasedPC,
  title={Octree-based Point-Cloud Compression},
  author={Ruwen Schnabel and R. Klein},
  booktitle={PBG@SIGGRAPH},
  year={2006}
}
In this paper we present a progressive compression method for point sampled models that is specifically apt at dealing with densely sampled surface geometry. The compression is lossless and therefore is also suitable for storing the unfiltered, raw scan data. Our method is based on an octree decomposition of space. The point-cloud is encoded in terms of occupied octree-cells. To compress the octree we employ novel prediction techniques that were specifically designed for point sampled geometry… 
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383 Citations
Highly Influential Citations
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Background Citations
135
Methods Citations
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Results Citations
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383 Citations

LEARNING-BASED LOSSLESS POINT CLOUD GEOMETRY CODING USING SPARSE REPRESENTATIONS

Experimental results show that the proposed context-based lossless point cloud geometry compression method outperforms the state-of-the-art geometry compression standard from MPEG with average rate savings of 52% on a diverse set of point clouds from four different datasets.

Learning-Based Lossless Point Cloud Geometry Coding Using Sparse Tensors

  • D. NguyenA. Kaup
  • Computer Science
    2022 IEEE International Conference on Image Processing (ICIP)
  • 2022
This paper proposes a context-based lossless point cloud geometry compression that directly processes the point representation and uses a sparse convolution neural network to estimate the voxel occupancy sequentially from the x, y, z input data.

Curve-Based Representation of Point Cloud for Efficient Compression

This paper investigates cloud point compression via a curve-based representation of the point cloud via a com- petition-based predictive encoder that includes different prediction modes to demonstrate the effectiveness of the proposed method.

Intra-Frame Compression of Point Cloud Geometry using Boolean Decomposition

This paper proposes a lossless intra coder of the geometry information of voxelized point clouds using well-known techniques for coding of bi-level images, as well as a novel boolean decomposition method to further exploit the redundancy within the geometry.

Point Cloud Compression with Sibling Context and Surface Priors

A new entropy model is proposed that explores the hierarchical dependency in an octree using the context of siblings' children, ancestors, and neighbors to encode the occupancy information of each non-leaf octree node into a bitstream.

Multiscale Point Cloud Geometry Compression

This paper proposes a multiscale end-to-end learning framework that hierarchically reconstructs the 3D Point Cloud Geometry (PCG) via progressive re-sampling on top of a sparse convolution based autoencoder for point cloud compression and reconstruction.

Point Cloud Geometry Compression Based on Multi-Layer Residual Structure

This work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture that effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume.

3D Point Cloud Attribute Compression Using Geometry-Guided Sparse Representation

Experimental results show that the proposed compression scheme for the attributes of voxelized 3D point clouds is able to achieve better rate-distortion performance and visual quality, compared with state-of-the-art methods.

An improved enhancement layer for octree based point cloud compression with plane projection approximation

This work presents an alternative enhancement layer to the coarse octree coded point cloud, in this case, the base layer of the point cloud is coded in known octree based fashion, but the higher level of details are coded in a different way in an enhancement layer bit-stream.

REAL-TIME COMPRESSION STRATEGY ON VARIOUS POINT CLOUD STREAMS

A spatial decomposition based on octree data structures is performed, which can successively extend the point clouds at the decoder by encoding their structural differences by reducing coding complexity and coding precision.
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