Latent 3D Image Volume for Object Pose Estimation
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Solution Overview
Problem
Current robotic systems face challenges in estimating the pose of objects without complete 3-D models, especially in realistic scenarios where access to 3-D models is limited, which hinders effective grasp and manipulation tasks.
Innovation Solution
An end-to-end neural network framework that reconstructs a latent 3-D representation of objects using a small number of reference views, allowing for pose estimation and rendering of objects from arbitrary views without additional training, utilizing a differentiable rendering pipeline and multi-view consistency to construct a latent representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete 3-D models are used for pose estimation, then measurement precision is improved, but device complexity and data requirements worsen
Solution Approach 1:
The patent creates a latent 3-D representation (a simplified copy) of the object from 2-D reference images instead of requiring complete 3-D models. This latent representation captures essential geometric features needed for pose estimation while being computationally efficient and easier to obtain than full 3-D models.
Solution Approach 2:
The patent replaces traditional geometric analysis and mechanical 3-D modeling approaches with a neural network-based latent representation system. The differentiable rendering pipeline substitutes complex geometric computations with learned representations that achieve comparable or superior pose estimation accuracy with reduced complexity.
2Measurement precision
If complete 3-D models are obtained, then pose estimation accuracy is improved, but loss of time in data acquisition worsens
Solution Approach 1:
The system performs preliminary reconstruction of a latent 3-D representation from a small number of reference images before pose estimation is needed. This pre-computed latent representation can then be used for rapid pose estimation without requiring time-consuming 3-D model acquisition or processing at the time of measurement.
Solution Approach 2:
Instead of acquiring and processing complete 3-D models which take significant time, the patent creates a simplified latent 3-D copy from minimal reference images. This copy retains sufficient information for accurate pose estimation while dramatically reducing the time required for data acquisition and processing.
3Ease of operation
If geometry-inspired heuristics are used to select grasp points, then ease of operation is improved, but measurement precision worsens
Solution Approach 1:
The patent introduces a latent 3-D representation as an intermediary between 2-D images and grasp analysis. This intermediate representation provides accurate geometric information for determining stable grasp points while maintaining ease of operation through automated neural network processing, bridging the gap between simple operation and precise measurement.
Data Source
AI summary
Apparatuses, systems, and techniques estimate a pose of an object based on images generated from a combined image volume. In at least one embodiment, the combined image volume is obtained from a plurality of image volumes generated based on a plurality of images of an object.