3D Point Cloud Labeling with 2D Projection Segmentation
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Solution Overview
Problem
Deep learning-based 3D point cloud semantic segmentation requires intensive well-labeled training data, which are often unavailable or lacking, and manual labeling is expensive and time-consuming, while existing automatic labeling technologies suffer from low accuracy.
Innovation Solution
A method and system that projects 3D point cloud data onto 2D images for unsupervised image segmentation, generating segmented masks, and reprojects them back to the 3D point cloud for labeling, allowing for efficient pre-labeling and re-labeling with reduced manual effort and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used on 3D point clouds, then labeling accuracy can be ensured, but time consumption and cost increase significantly
Solution Approach 1:
The patent segments the 3D point cloud labeling task into multiple 2D projection views. By dividing the complex 3D space into several 2D planes, the system enables parallel processing and reduces the temporal complexity of manual labeling while maintaining accuracy through multi-view verification.
Solution Approach 2:
The patent introduces 2D projection images as an intermediary between the raw 3D point cloud and the final labeling result. This intermediary layer allows annotators to work on simplified 2D representations while the system automatically reconstructs and validates labels in the original 3D space, reducing direct manual intervention time.
2Productivity
If automatic labeling technologies are used, then time consumption is reduced, but labeling accuracy decreases
Solution Approach 1:
The patent implements a feedback mechanism where initial automatic labels from 2D projections are validated and refined by checking consistency across multiple projection views. Discrepancies trigger automated refinement processes or highlight specific regions for minimal manual verification, creating a closed-loop system that improves accuracy while maintaining efficiency.
Solution Approach 2:
The patent transforms the labeling problem from 3D space to multiple 2D dimensions, performs segmentation independently in each dimension, then synthesizes results back to 3D. This dimensional transformation enables the use of more成熟 2D segmentation algorithms while preserving 3D spatial relationships through coordinate transformation and consistency verification.
3Extent of automation
If 3D unsupervised clustering is used for automatic labeling, then some automation is achieved, but accuracy remains low
Solution Approach 1:
The patent replaces direct 3D spatial clustering with a 2D image processing pipeline that uses established computer vision techniques. By substituting the mechanical 3D clustering approach with 2D projection-based segmentation followed by coordinate transformation, the system leverages more accurate and mature 2D algorithms while maintaining automation.
Data Source
AI summary
The present application relates to labeling methods and systems for 3D point cloud segmentation. The labeling method comprises steps of importing 3D point cloud data; determining a scenario of the 3D point cloud data; and if the scenario is that the 3D point cloud data includes no prior labeled data, adopting a 2D-image-based workflow to label the 3D point cloud data. In the 2D-image-based workflow, the 3D point cloud data is projected on 2D images to generate segmented masks under unsupervised image segmentation. The scenarios can comprise the 3D point cloud data includes sufficient labeled data and the 3D point cloud data includes insufficient labeled data. This present application also provides related processing steps. The labeling methods and systems can significantly reduce the time and manual effort for 3D point cloud semantic segmentation, allow an iterative process to conduct pre-labeling and re-labeling and increase accuracy at the same time.