Scalable Point Cloud Compression via Deep Neural Network Segmentation
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing and processing large-scale point cloud data, particularly in real-time applications such as autonomous driving and virtual reality, due to the computational expense and storage requirements of handling millions to hundreds of millions of points.
Innovation Solution
A scalable coding framework is proposed that converts point cloud data into a coarser representation with pointwise features, allowing for efficient encoding and decoding. This framework uses a base layer for the coarser point cloud and an enhancement layer for the pointwise features, leveraging deep neural networks and sparse tensor operations to achieve lossy compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using traditional methods, then storage requirements are reduced, but computational expense increases for real-time processing
Solution Approach 1:
The point cloud data is segmented into multiple versions with different levels of detail. A first version contains coarse geometric information while a second version contains refined details. This segmentation allows selective processing where only necessary refinement operations are performed, reducing overall computational expense while maintaining storage efficiency.
Solution Approach 2:
The framework performs preliminary encoding of the first version of the point cloud before refinement. By establishing the base geometric structure first and then applying refinement operations, the system avoids the computational burden of processing all points at full detail, thereby reducing computational expense while achieving effective compression.
2Quantity of substance
If point cloud data is compressed to reduce storage, then storage requirements decrease, but processing time increases
Solution Approach 1:
The point cloud is divided into a first version for base geometry and a second version for refinement details. This segmentation enables parallel processing where the first version can be processed independently and quickly, while refinement operations on the second version are performed selectively, thereby reducing overall processing time while maintaining storage compression.
Solution Approach 2:
The framework applies refinement operations partially rather than to all points. By determining which points require refinement based on their importance and applying refinement selectively, the system reduces processing time while still achieving effective storage compression through the selective encoding of refinement information.
3Measurement precision
If refinement information is obtained for all points, then quality is improved, but computational expense increases
Solution Approach 1:
The framework applies refinement operations locally to specific points rather than uniformly to all points. By identifying which points benefit most from refinement and applying refinement information selectively to those regions, the system maintains high quality where needed while reducing computational expense in less critical areas.
Solution Approach 2:
The system changes the parameter of refinement application from global to local based on point importance. By adjusting which points receive refinement information based on their geometric significance and spatial location, the framework maintains measurement precision for critical points while reducing overall computational expense through selective processing.
Data Source
AI summary
In one implementation, we propose a lossy point cloud compression scheme to encode point cloud geometry with deep neural networks. The encoder first encodes a coarser version of the input point cloud as a bitstream. Then it represents the residual (fine geometry details) of the input point cloud as pointwise features of the encoded coarser point cloud, followed by encoding the features as the second bitstream. On the decoder side, the coarser point cloud is firstly decoded from the first bitstream. Then its pointwise features are decoded. In the end, the residual is decoded from the pointwise features and added back to the coarser point cloud, leading to a high-quality decoded point cloud. The encoding and/or decoding of the features can be further augmented with feature aggregation, such as transformer blocks.