Point Cloud Shape Completion via Voxel Segmentation and Class Integration
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Solution Overview
Problem
Existing shape completion methods face challenges in accurately completing point clouds due to high computational costs of three-dimensional convolutions, which lead to inefficiencies in processing and utilizing information from point groups and individual points.
Innovation Solution
A shape completion device and method that incorporates a class identification unit to generate a shape completion point cloud by integrating global features with class identification features, using a neural network structure that includes a class identifier, generator, and determiner to optimize the completion process, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional convolutions are used for shape completion, then shape completion accuracy can be improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the shape completion task into two distinct stages: (1) a coarse shape prediction stage using a three-dimensional encoder-predictor network on voxelized input, and (2) a detailed surface reconstruction stage using a local encoder-predictor network. This segmentation allows the computationally intensive three-dimensional convolutions to be applied only once at coarse resolution, while the final detailed reconstruction uses more efficient local operations, thereby reducing overall computational cost while maintaining accuracy.
Solution Approach 2:
The patent transitions from direct point cloud processing to a multi-dimensional representation approach. It first voxelizes the point cloud into a three-dimensional grid (adding a volumetric dimension), performs global shape prediction in this voxel space, then reconstructs detailed surfaces by synthesizing volumetric patches. This dimensionality change enables efficient use of three-dimensional convolutions at appropriate scales while avoiding their computational burden at fine detail levels.
2Measurement precision
If point cloud data is processed in detail to improve completion accuracy, then shape completion quality improves, but processing time increases
Solution Approach 1:
The patent divides the processing pipeline into two temporal stages: a fast coarse prediction phase that quickly estimates overall shape using three-dimensional convolutions on voxelized data, followed by a more detailed but computationally efficient local reconstruction phase. This time-segmented approach ensures that detailed processing is applied only where necessary, reducing total processing time while maintaining high completion quality.
Solution Approach 2:
The patent performs preliminary coarse shape prediction and global structure inference before undertaking detailed surface reconstruction. By first establishing the overall shape framework through efficient three-dimensional encoding, subsequent detailed processing can focus only on refining local features, significantly reducing the time required for high-quality completion compared to processing all details from scratch.
3Productivity
If coarse grid voxelization is used to reduce computational cost, then processing efficiency improves, but ability to utilize point cloud information effectively deteriorates
Solution Approach 1:
The patent introduces an intermediate voxel representation dimension that preserves point cloud information without requiring fine-grained processing. By voxelizing at coarse resolution for global prediction, the method captures essential shape information efficiently. The original point cloud data is then directly utilized in the local reconstruction stage, combining the efficiency of voxel-based processing with the information richness of direct point cloud analysis.
Solution Approach 2:
The patent applies different information processing strategies to different aspects of the data: coarse voxelization for global shape understanding (where efficiency is paramount) and direct point cloud processing for local detail reconstruction (where information utilization is critical). This segmented approach ensures that computational efficiency and information preservation are optimized for their respective processing stages.
Data Source
AI summary
It is possible to receive a point cloud as input and perform shape completion with high accuracy. A shape completion unit inputs an input point cloud and a class identification feature output by a class identification unit to a generator that is learned in advance and generates a shape completion point cloud that is to complete a point cloud and is a set of three-dimensional points by receiving, as input, the point cloud and the class identification feature, gaining an integration result obtained by integrating a global feature that is a global feature based on local features extracted from respective points of the point cloud with the class identification feature, and convoluting the integration result, and outputs the shape completion point cloud that is to complete the input point cloud.