Category Agnostic Articulated Object Reconstruction via AI Latent Codes
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Solution Overview
Problem
Current computer vision systems face challenges in reconstructing three-dimensional shapes and estimating six-degrees-of-freedom pose and sizes of articulated objects from a single visual observation, especially for objects with varying kinematics, as existing methods are complex, error-prone, and not capable of real-time implementation across multiple categories.
Innovation Solution
The Category and Joint Agnostic Reconstruction of Articulated Objects (CARTO) system uses a trained AI encoder and decoder to generate joint codes and shape codes from images, allowing for real-time identification and reconstruction of articulated objects without prior interaction with the environment, using stereo images or RGB-D images, and inferring latent information to facilitate grasping and manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing computer vision methods are used for articulated object reconstruction, then shape and pose information can be obtained, but the system complexity increases and real-time performance is not achieved
Solution Approach 1:
The patent replaces complex mechanical reconstruction systems with an AI-based encoder-decoder neural network system. The encoder processes input images to extract features, and the decoder reconstructs 3D shape and pose information, substituting traditional multi-step geometric processing with learned representations that achieve real-time performance while maintaining accuracy.
Solution Approach 2:
The patent transforms the reconstruction problem by changing parameters from direct 3D coordinate estimation to latent space code prediction. The network learns to predict compact latent codes that encode shape and pose information, which are then decoded into 3D representations, simplifying the overall system while improving efficiency.
2Adaptability or versatility
If traditional reconstruction methods are applied to multiple object categories, then comprehensive coverage is achieved, but the method becomes error-prone and computationally intensive
Solution Approach 1:
The patent creates a universal encoder-decoder framework that can process multiple object categories simultaneously. The learned latent representations are category-agnostic, allowing the same network architecture to reconstruct diverse articulated objects (robots, animals, mechanical structures) without category-specific tuning, improving both versatility and reliability.
Solution Approach 2:
The patent introduces latent space codes as an intermediary between input images and 3D reconstruction output. These latent codes serve as a mediating representation that captures essential shape and pose information in a compressed form, reducing errors propagated from input variations and improving reconstruction reliability across different categories.
3Ease of operation
If detailed joint information is extracted from images, then manipulation capability is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential joint-related information needed for manipulation by the decoder network from the full image input. The latent codes selectively encode relevant pose and configuration data, discarding redundant visual details, thus providing sufficient manipulation information with reduced processing time.
Solution Approach 2:
The encoder performs preliminary feature extraction and compression into latent codes before the decoding stage. This preliminary processing organizes and condenses joint information in advance, allowing the decoder to quickly generate manipulation-ready outputs without reprocessing raw image data, thereby reducing overall processing time.
Data Source
AI summary
Aspects of the present disclosure provide techniques for category and joint agnostic reconstruction of articulated objects. An example method includes obtaining images of an environment having objects and generating, using a trained AI encoder, first information associated with the images based at least in part on the images, the first information comprising a plurality of joint codes and a plurality of shape codes associated with the images. The method further includes generating, using a trained AI decoder, second information associated with the objects based at least in part on the plurality of joint codes and the plurality of shape codes, the second information comprising shape information, one or more joint types, and one or more joint states corresponding to at least one of the objects. The method further includes storing the second information in memory.