3D Object Representation Using Point Clouds and Image Attributes
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Solution Overview
Problem
Existing techniques for generating 3D representations of objects in physical environments are inadequate in capturing and utilizing these representations efficiently and accurately.
Innovation Solution
A method that combines 3D point cloud and 2D image inputs to generate a refined 3D representation of objects, using 3D point cloud for part segmentation and orientation, and 2D image input for attribute recognition, resulting in a smoother and more accurate representation without requiring computationally expensive 3D meshing algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If computationally expensive 3D meshing algorithms are used, then manufacturing precision of 3D representation is improved, but productivity is worsened
Solution Approach 1:
The patent segments the 3D representation task into two distinct components: (1) using 3D point cloud data to determine geometric parameters (position, size, orientation) of object parts, and (2) using 2D image data to determine visual attributes (color, texture, material) of object parts. This segmentation allows each component to be processed independently and efficiently, avoiding the computational burden of traditional 3D meshing algorithms while maintaining high representation accuracy.
Solution Approach 2:
The patent introduces an intermediary approach by using 2D image data as a mediator to supplement 3D point cloud data. Instead of directly processing noisy 3D point cloud data through expensive meshing algorithms, the system uses 2D images to capture visual attributes and combines them with 3D geometric information, achieving high-fidelity 3D representations through a computationally efficient intermediary pathway.
2Manufacturing precision
If traditional 3D meshing algorithms are used, then 3D representation accuracy is improved, but device complexity is worsened
Solution Approach 1:
The patent divides the complex 3D representation task into simpler sub-tasks: geometric parameter extraction from 3D point cloud and attribute extraction from 2D images. This segmentation reduces algorithmic complexity by replacing the monolithic 3D meshing process with two independent, simpler processing streams that can be executed more efficiently.
Solution Approach 2:
The patent substitutes the mechanical 3D meshing process with a hybrid approach combining 3D point cloud processing and 2D image processing. Instead of relying solely on complex 3D geometric operations, the system replaces part of the processing mechanism with 2D image analysis, which is computationally simpler and achieves comparable or superior results.
3Productivity
If only 3D point cloud data is used, then processing speed is improved, but measurement precision is worsened
Solution Approach 1:
The patent merges 3D point cloud data and 2D image data into a unified processing framework. The 3D point cloud provides accurate geometric information (position, size, orientation) for real-time processing, while 2D images provide detailed visual attributes (color, texture, material). By combining these complementary data sources, the system achieves both high processing speed and high measurement precision simultaneously.
Solution Approach 2:
The patent applies local quality by using different data sources for different properties: 3D point cloud data is used for geometric parameters where precision and speed are critical, while 2D image data is used for visual attributes where detailed recognition is needed. This localized application of different data types optimizes both processing efficiency and measurement accuracy for each specific property.
Data Source
AI summary
Various implementations provide 3D representations of objects. Such representations may be based on 3D point cloud and/or 2D image inputs that are obtained based on sensor data, e.g., images, depth data, motion data, etc. 3D point cloud input may be used for part segmentation and/or to determine position and/or orientation of object parts, e.g., generating 3D bounding boxes representing the sizes, positions, and orientations, of object parts. 2D image input may be used for part attribute recognition, e.g., to determine whether a chair legs part has a particular type such as star-shaped, straight down, crossed-shaped, etc. Part attributes may be used to produce a relatively simple and relatively accurate representation of the shape of each part within a respective area, e.g., within a bounding box determined for each part using the 3D point cloud input.