Interlaced 2D-3D Transformer for Weakly Supervised Point Cloud Segmentation
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Solution Overview
Problem
Annotating data for training neural networks in 3D image data, such as point clouds, is time-consuming and expensive, making it impractical or impossible in some cases, and existing methods struggle with the need for per-point annotations and capturing detailed semantic clues from textureless point clouds.
Innovation Solution
The Multimodal Interlaced Transformer (MIT) uses weak supervision with scene-level class tags, combining 3D and 2D data through encoders and a decoder that perform interlaced 2D-3D cross-attention, implicitly fusing features without per-point annotations, leveraging texture information from 2D images to enrich 3D geometric features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If per-point annotations are used for training neural networks on 3D point clouds, then segmentation accuracy is improved, but annotation time and cost increase significantly
Solution Approach 1:
The patent uses 2D images as copies or projections of the 3D point cloud data. These 2D projections contain semantic information that can be annotated more efficiently, and then transferred back to enrich the 3D segmentation, avoiding the need for direct per-point annotation in 3D space.
Solution Approach 2:
The patent transforms the annotation problem from 3D space to 2D space by creating projections of the point cloud onto 2D planes. Annotation is performed on these 2D projections where it is more efficient, and the results are then mapped back to the original 3D data, effectively solving the annotation bottleneck.
2Loss of information
If per-point annotations are required for 3D point cloud segmentation, then detailed semantic information is captured, but the complexity and cost of data preparation increases
Solution Approach 1:
The patent merges 2D and 3D processing pipelines by combining 2D image projections with 3D point cloud data. The 2D projections provide semantic clues that are integrated with the 3D geometric information, allowing detailed semantic segmentation without requiring equally detailed 3D annotations.
Solution Approach 2:
The 2D projections serve as an intermediary between the 3D point cloud and the annotation process. Instead of annotating 3D points directly, the system annotates 2D projections which then mediate the transfer of semantic information back to the 3D space, reducing preparation complexity.
3Measurement precision
If 3D point cloud data alone is used for segmentation, then geometric accuracy is maintained, but semantic understanding of textureless regions is limited
Solution Approach 1:
The patent projects 3D point cloud data onto 2D planes to create views that capture texture and semantic information. These 2D projections reveal visual patterns and textures that are not apparent in the raw 3D point cloud, enriching the semantic understanding of textureless regions.
Solution Approach 2:
The system makes the 2D projection module universally applicable to any 3D point cloud data. The same projection and annotation transfer mechanism works across different datasets and scenarios, providing a general solution for enriching 3D semantic information without losing geometric accuracy.
Data Source
AI summary
Apparatuses, systems, and techniques to generate annotations for at least one three-dimensional representation corresponding to a scene based at least in part on at least one two-dimensional image depicting the scene. In at least one embodiment, a set of scene-level labels associated with at least one training scene are used to weakly supervise training of one or more neural networks used to generate the annotations.