Semantic Fusion for 3D Object Annotation Accuracy
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Solution Overview
Problem
Current virtual reality systems face challenges in accurately identifying and rendering 3D objects within virtual environments, leading to inconsistencies and errors in object recognition and placement, which limits the effectiveness of semantic 3D datasets and machine learning algorithms.
Innovation Solution
A closed-loop workflow that includes segmentation-aided free-form mesh labeling, human-aided geometry correction, and a bootstrapping annotation scheme to enhance semantic propagation between 2D and 3D, using algorithms like Mask-RCNN for semantic instance prediction and integrating predictions into mesh segmentation to improve annotation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If segmentation-based algorithms are used for object identification, then annotation efficiency is improved, but annotation accuracy deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the annotation process into multiple specialized stages: initial segmentation using algorithms like Mask-RCNN, followed by refinement through human-aided geometry correction and bootstrapping annotation. This multi-stage segmentation approach allows efficient initial processing while maintaining pathways for accuracy improvement.
Solution Approach 2:
The patent implements feedback mechanisms through its closed-loop workflow where annotation results are continuously refined. Human-aided geometry correction provides feedback on initial algorithmic segmentations, and bootstrapping annotation uses accumulated accurate annotations to improve future segmentation performance, creating a self-improving system that maintains both efficiency and accuracy.
2Speed
If automated segmentation algorithms are used, then processing speed is improved, but rendering accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by using automated segmentation algorithms to quickly generate initial annotations and geometric corrections before final rendering. This preliminary processing captures the majority of objects efficiently, while subsequent refinement steps address only the critical accuracy requirements, maintaining overall processing speed while improving rendering precision.
Solution Approach 2:
The patent implements local quality by applying different processing intensities to different regions: highly automated processing for clear, unambiguous objects and more intensive human-aided refinement for complex or ambiguous regions. This localized approach maintains processing speed for straightforward cases while ensuring rendering accuracy for challenging cases.
3Device complexity
If simple segmentation methods are used, then system complexity is reduced, but annotation reliability deteriorates
Solution Approach 1:
The patent segments the annotation system into modular components: initial segmentation module, geometry correction module, bootstrapping annotation module, and integration module. This segmentation allows each component to be independently optimized and validated, improving overall reliability while keeping individual module complexity manageable.
Solution Approach 2:
The patent introduces intermediary processes between simple segmentation and final reliable annotations. Human-aided geometry correction acts as an intermediary that validates and corrects algorithmic output, while bootstrapping annotation serves as an intermediary that progressively builds reliability from initial annotations. These intermediaries bridge the gap between simple processing and high reliability without requiring complete system redesign.
Data Source
AI summary
In one embodiment, a computing system accesses a plurality of images captured by one or more cameras from a plurality of camera poses. The computing system generates, using the plurality of images, a plurality of semantic segmentations comprising semantic information of one or more objects captured in the plurality of images. The computing system accesses a three-dimensional (3D) model of the one or more objects. The computing system determines, using the plurality of camera poses, a corresponding plurality of virtual camera poses relative to the 3D model of the one or more objects. The computing system generates a semantic 3D model by projecting the semantic information of the plurality of semantic segmentations towards the 3D model using the plurality of virtual camera poses.