Source-linked AI summary
Multiscale Point Cloud Geometry Compression
Jianqiang Wang, Dandan Ding, Zhu Li, Zhan Ma
TL;DR
Compressing sparse, unstructured, high-precision point-cloud geometry efficiently is difficult. The paper proposes a sparse-convolutional autoencoder with progressive multiscale resampling, separately coding latent geometry and features. It reports over 40% and 70% BD-Rate gains against MPEG V-PCC and G-PCC, respectively, with substantially lower reconstruction complexity.
Problem
Efficiently compressing sparse, unstructured, high-precision point-cloud geometry remains challenging for communication and networked applications.
Method
A sparse-convolutional autoencoder progressively downsamples and upsamples point clouds, separately compressing latent geometry and feature attributes.
Results
More than 40% and 70% BD-Rate gains are reported against MPEG V-PCC and G-PCC, respectively, with over 30% savings versus Learned-PCGC.
Takeaways & Limitations
Multiscale resampling and sparse convolutions reduce complexity while preserving geometry details in point-cloud compression.
Takeaways & Limitations
Extending the approach to sparse point clouds such as LiDAR remains future work.
Abstract
from arXiv · showhide
Recent years have witnessed the growth of point cloud based applications because of its realistic and fine-grained representation of 3D objects and scenes. However, it is a challenging problem to compress sparse, unstructured, and high-precision 3D points for efficient communication. In this paper, leveraging the sparsity nature of point cloud, we propose a multiscale end-to-end learning framework which hierarchically reconstructs the 3D Point Cloud Geometry (PCG) via progressive re-sampling. The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction. For the input PCG which has only the binary occupancy attribute, our framework translates it to a downscaled point cloud at the bottleneck layer which possesses both geometry and associated feature attributes. Then, the geometric occupancy is losslessly compressed using an octree codec and the feature attributes are lossy compressed using a learned probabilistic context model.Compared to state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based PCC (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG), our method achieves more than 40% and 70% BD-Rate (Bjontegaard Delta Rate) reduction, respectively. Its encoding runtime is comparable to that of G-PCC, which is only 1.5% of V-PCC.
1 Introduction
The paper targets efficient compression of sparse, unstructured point-cloud geometry for networked 3D applications. It proposes a sparse, multiscale learning framework intended to improve compression efficiency and reduce computational cost.
- Motivation: Point clouds represent 3D objects and scenes realistically through unstructured points sparsely distributed in 3D space.This representation supports applications including augmented reality and autonomous driving.
- Existing approaches: MPEG standardizes two representative approaches: V-PCC projects 3D content into 2D video planes, whereas G-PCC directly encodes 3D models.G-PCC uses models such as octrees or triangle surfaces.
- Existing approaches: Learned-PCGC improves rate-distortion performance but applies dense 3D convolutions uniformly across binary occupancy blocks, despite most voxels being null.This limits efficient exploitation of point-cloud sparsity.
- Proposed approach: The proposed framework progressively downsamples and upsamples point clouds using sparse convolutions to embed local geometry into a compact latent representation.At the bottleneck, geometry and associated feature attributes are processed separately.
- Results: More than 40% and 70% BD-Rate gains are reported against MPEG V-PCC and G-PCC, respectively, on common MPEG and JPEG test point clouds.The method also reports more than 30% BD-Rate saving and reduced complexity relative to Learned-PCGC.
2 Related Works
Prior work includes model-based MPEG codecs, learning-based autoencoders, and sparse-convolution methods. The paper positions sparse convolution as a way to reduce computation on positively occupied point-cloud voxels.
- MPEG geometry coding: Octree decomposition directly models point-cloud geometry and is used by MPEG G-PCC as an octree geometry codec.G-PCC also supports trisoup geometry coding for approximating dense 3D surfaces.
- MPEG video coding: MPEG V-PCC projects 3D content into 2D planes so video codecs can encode projected planes and depth maps.The passage describes V-PCC as especially efficient for dense point clouds.
- Learning-based compression: Learning-based PCG compression commonly adapts 2D image-compression autoencoders by replacing 2D convolutions with 3D convolutions.Several approaches represent voxelized point-cloud occupancy and train reconstruction as binary classification with BCE loss.
- Sparse convolution: Sparse convolution represents tensors with occupied coordinates and associated features, aggregating features only at positively occupied coordinates.This selective computation reduces complexity and supports large-scale point-cloud processing.
- Framework notation: Figure 1 identifies the framework’s main operators and modules, including sparse convolution, scaling, quantization, arithmetic coding, ReLU, and IRN feature aggregation.IRN denotes the Inception-Residual Network unit.
3 Multiscale PCG Compression
The proposed codec progressively resamples sparse point clouds through multiscale sparse-convolutional encoding and hierarchical decoding. It preserves latent geometry separately from feature attributes while refining geometry coarse-to-fine.
- Multiscale representation: The encoder progressively downsamples the sparse input into multiple scales, embedding local occupancy structure into latent feature components.The bottleneck contains occupied coordinates and associated features, with coordinates serving as coarse structural keypoints.
- Network design: Sparse convolutions and IRN blocks are used around repeated downscaling and upscaling steps to reduce tensor-processing complexity and extract features efficiently.The encoder halves each geometric dimension at each stride-two downscaling step.
- Hierarchical reconstruction: The decoder upsamples lower-scale reconstructions and progressively refines geometry by binary classification, pruning false voxels and retaining predicted occupied voxels.Each finer reconstruction depends on the preceding lower-scale result.
- Occupancy classification: Binary cross-entropy trains voxel-occupancy probabilities, and inference selects top-k voxels to adjust reconstructed point-cloud density.The target labels indicate occupied or empty voxels, while k can be matched to the ground-truth point count at each scale.
- Optimization: The multiscale objective combines scale-dependent BCE distortion with a rate term for the compressed quantized latent features.The Lagrangian is Jloss = R + λD.
- Latent compression: Latent geometry coordinates are losslessly octree-coded, while latent feature attributes are quantized and lossy-compressed before entropy coding.Geometry is preserved because distortion of the latent skeleton keypoints can severely degrade quality.
4 Experimental Results
The evaluation measures rate-distortion performance, runtime, and resource use on ten MPEG/JPEG test point clouds. The method achieves substantial BD-Rate gains while maintaining encoding complexity near G-PCC and below V-PCC.
- Evaluation setup: Ten dense point clouds from 8iVFB, Owlii, and MVUB were selected to cover different scales and structures.The evaluation follows common MPEG and JPEG test-point-cloud usage.
- Scope: Extending the approach to sparse point clouds such as LiDAR remains future work.The reported experiments use dense point clouds from established benchmark collections.
- Evaluation setup: Bit rate uses bits per input point, while distortion uses D1 and D2 point-to-point or point-to-plane MSE-based measurements.Rate-distortion curves and BD-Rate gains are calculated for comparison.
- Rate-distortion results: >80% BD-Rate gains over G-PCC octree and >70% over G-PCC trisoup were achieved on average.The method also obtains >40% BD-Rate improvement over V-PCC and >30% over Learned-PCGC.
- Visual quality: The reconstruction preserves geometry details more smoothly than the compared methods, while V-PCC shows seams and G-PCC can lose details.For Redandblack, the method uses 0.026 bpp for coordinates and 0.130 bpp for feature attributes.
- Complexity: Encoding runtime is close to the fastest G-PCC octree codec and much faster than V-PCC.Encoding and decoding require approximately 333 MB and 1,273 MB of GPU memory, respectively, versus at least 5 GB for Learned-PCGC.
5 Discussion
Multiscale reconstruction reduces computational and memory costs by progressively pruning null voxels, while adaptive probabilistic contexts improve latent-feature compression. The multiscale design substantially limits voxel growth compared with single-scale reconstruction.
- Multiscale Reconstruction: Multiscale reconstruction prunes null voxels between scales, making runtime and memory costs lower than with single-scale reconstruction.Lower-scale geometry is upscaled and refined by classification, so fewer intermediate voxels require processing.
- Multiscale Reconstruction: Single-scale reconstruction requires almost 4× more space, causing around 2.4× FLOPs, 4× running memory, and 3× overall runtime costs.These costs result from processing additional voxels at the largest reconstruction scale.
- Adaptive Contexts Conditioned on Priors: The latent feature representation is modeled with a conditional Gaussian mixture model whose parameters are predicted from autoregressive or hyper priors.The priors provide structural contexts and can offer additional BD-Rate gains.
6 Conclusion
The work combines multiscale resampling and sparse convolutions to exploit point-cloud sparsity for compact, efficient geometry compression. It reports more than 40% BD-Rate gains over MPEG V-PCC and identifies color and LiDAR compression as future directions.
- 6 Conclusion: The method uses multiscale resampling to leverage point-cloud sparsity for compact feature representation and sparse convolutions to reduce sparse-tensor processing complexity.The conclusion describes these components as improving efficiency and reducing space and time complexity.
- 6 Conclusion: More than 40% BD-Rate gains over state-of-the-art MPEG V-PCC are reported on common test sequences.The conclusion also characterizes the model as reliable and robust in training and inference with reduced complexity consumption.
- 6 Conclusion: Future work includes extending the method to color attributes and compressing LiDAR point clouds with sparser distributions.These directions broaden the method beyond geometry-only compression.