Point Cloud Encoder Training via Invariant Feature Segmentation
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Solution Overview
Problem
Conventional methods for processing three-dimensional point cloud data are inefficient, resource-intensive, and struggle with large datasets, often relying on volumetric representations that are limited by resolution and computational cost, making them time-consuming and not user-friendly.
Innovation Solution
A method that trains an encoder by inputting sample point cloud data to determine invariant and variable portions, calculating similarity and spatial losses, and adjusting the encoder's parameters to improve processing efficiency and accuracy, allowing for efficient processing of point cloud data while saving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If volumetric representations are used for point cloud data processing, then processing can be performed, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the point cloud data processing into two distinct parts: volumetric representation processing (for invariant features) and surface representation processing (for variable features). This segmentation allows each part to be processed using appropriate methods, avoiding the need to process the entire point cloud using computationally intensive volumetric methods, thus reducing overall computational cost while maintaining processing capability.
Solution Approach 2:
The patent extracts invariant features from the point cloud data by projecting them onto a two-dimensional plane, separating them from the variable features that require three-dimensional surface processing. This extraction allows the invariant features to be processed more efficiently without the computational burden of full 3D volumetric analysis, thereby reducing energy consumption while preserving processing effectiveness.
2Ease of manufacture
If manual feature production is used for specific tasks, then feature extraction is straightforward, but adaptability to different tasks is limited
Solution Approach 1:
The patent creates a universal feature extraction system that can handle multiple tasks simultaneously. By extracting both invariant features (through 2D projection) and variable features (through 3D surface analysis), the system provides a multi-functional approach that adapts to different processing requirements without requiring separate manual feature production for each task, thus improving task adaptability while maintaining ease of operation.
3Measurement precision
If high resolution is used in volumetric representations, then processing accuracy improves, but computational requirements increase
Solution Approach 1:
The patent segments the feature representation into two components with different resolution requirements: invariant features are processed at lower resolution through 2D projection, while only the necessary variable features are processed at high resolution through 3D surface analysis. This segmentation allows the system to achieve high processing accuracy for critical features without the computational burden of high-resolution volumetric processing of the entire point cloud.
Solution Approach 2:
The patent applies local quality by providing high-resolution processing only where necessary (for variable features on the object surface) while using lower-resolution processing for invariant features. This localized approach to quality ensures that computational power is allocated efficiently to areas that require it, maintaining processing accuracy for critical features while reducing overall computational power requirements.
Data Source
AI summary
Embodiments relate to a method, an electronic device, and a program product for training an encoder and processing data. The method includes inputting sample point cloud data for an object to an encoder to obtain encoded data for the object, and determining, by transforming the encoded data, a plurality of invariant portions for the object and a plurality of variable portions for the object, an invariant portion in the plurality of invariant portions indicating an invariant feature of the object and a variable portion in the plurality of variable portions indicating a variable feature of the object. The method further includes determining, based on the plurality of invariant portions and the plurality of variable portions, a similarity loss and a spatial loss for the sample point cloud data, and adjusting, based on the similarity loss and the spatial loss, a parameter of the encoder to obtain a trained encoder.