Object-Centric Representation Learning With RGB-D Receptive Fields
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Solution Overview
Problem
Existing object understanding systems face inefficiencies in multi-dimensional pose estimation and require manually intensive training datasets, leading to ambiguities in object analysis and pose estimation due to the subjective nature of labeling.
Innovation Solution
A computer-implemented method utilizing RGB and depth information to learn an object-centric representation through multi-resolution receptive fields processed by convolutional encoders, eliminating the need for complex multi-dimensional pose estimation and intensive dataset training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually intensive training of multiple datasets is used for multi-dimensional pose estimation, then object understanding capability is improved, but training time and computational resources are excessively consumed
Solution Approach 1:
The patent segments the object representation into object-centric features (OCFs) that decompose complex objects into constituent parts with their spatial relationships. This segmentation allows the system to learn object understanding from multiple views without requiring exhaustive multi-dimensional pose estimation training, thereby reducing training time while maintaining comprehension accuracy.
Solution Approach 2:
The patent introduces a new dimensional approach by creating multi-view projections (front, back, left, right, top, bottom) and representing them in a unified object-centric feature space. This dimensional transformation enables the system to capture comprehensive object understanding without relying on traditional multi-dimensional pose estimation, significantly reducing training complexity and time.
2Measurement precision
If multi-dimensional pose estimation is performed for object identification, then pose accuracy is improved, but system complexity and computational load increase
Solution Approach 1:
The patent extracts essential pose information from multi-view images by generating object-centric features that capture spatial relationships and geometric properties directly. Instead of performing complex multi-dimensional pose estimation, the system extracts key pose-relevant features (occlusion patterns, visibility, spatial arrangement) that are sufficient for accurate pose understanding with reduced computational complexity.
Solution Approach 2:
The patent creates simplified copies of pose information through object-centric feature representations that encode spatial relationships and geometric properties in a compact form. These OCF copies retain the essential pose understanding capability while avoiding the computational burden of full multi-dimensional pose estimation, thereby reducing system complexity.
3Adaptability or versatility
If subjective labeling is used for object based information, then labeling flexibility is improved, but ambiguity in object analysis increases
Solution Approach 1:
The patent incorporates feedback mechanisms where object-centric features are learned from multiple annotated views and iteratively refined. The system uses consistency checks across different views and perspectives to resolve ambiguities in subjective labeling, ensuring that the final object representation maintains both labeling flexibility and reliability by cross-validating annotations from multiple angles.
4Measurement precision
If comprehensive object analysis is performed, then object understanding depth is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary processing by generating object-centric features from multiple views during the training phase and organizing them in advance. This preliminary action pre-computes and stores essential object representations that can be quickly retrieved and processed during inference, thereby maintaining deep object understanding while significantly improving processing speed for real-time applications.
Data Source
AI summary
A system and method for multimodal object-centric representation learning that include receiving data associated with an image and a depth map of an object. The system and method also include determining an object-surface point cloud based on the image and the depth map. The system and method additionally include determining multi-resolution receptive fields based on the object-surface point cloud. The system and method further include passing the multi-resolution receptive fields through convolutional encoders to learn an object centric representation of the object.