Stereo Geometry Perception for Unknown Object Manipulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training autonomous agents to perceive and manipulate unknown objects in diverse environments are laborious and costly due to the need for large-scale annotated datasets, and perception models trained on simulated RGB data often over-fit to simulation artifacts, struggling with reflective and transparent objects in real-world environments.
Innovation Solution
A lightweight neural network model (SimNet) trained on simulated stereo data focuses on geometric features using domain-randomized data, leveraging low-level vision features for high-level tasks like segmentation masks and 3D bounding boxes, without relying on active depth sensing or photorealistic simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale annotated datasets are used to train perception models, then object identification accuracy is improved, but training cost and time consumption increase significantly
Solution Approach 1:
The system pre-trains the neural network on synthetic stereo data with simulated geometric features before deploying it for real object manipulation tasks. This preliminary training on artificially generated data with known ground truth enables the model to learn robust geometric representations without requiring extensive real-world annotated datasets, thereby reducing training time while maintaining accuracy.
Solution Approach 2:
The patent uses synthetic stereo image pairs that copy and simulate real-world geometric scenarios. By training on these copied synthetic representations with known 3D structures and disparity maps, the system learns to generalize to real objects without needing to collect and annotate large amounts of real data, thus improving accuracy efficiently.
2Illumination intensity
If photorealistic simulation is used to train perception models, then visual realism is improved, but simulation artifacts increase causing over-fitting
Solution Approach 1:
The patent extracts and focuses specifically on geometric features (disparity maps, depth information, 3D structure) from synthetic stereo images, separating these critical manipulation-related features from photorealistic visual details. By taking out only the essential geometric information needed for manipulation tasks, the model learns robust representations that generalize to real environments without over-fitting to simulation-specific visual artifacts.
Solution Approach 2:
The system changes the training parameters from photorealistic visual fidelity to geometric accuracy by using synthetic stereo pairs with known ground truth disparity maps. The training optimizes for geometric feature extraction accuracy rather than visual realism, which improves reliability for real-world manipulation while reducing dependence on photorealistic simulation details.
3Measurement precision
If active depth sensing is used to improve 3D perception, then depth accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent replaces active depth sensing hardware (such as LIDAR or structured light sensors) with a computational approach using stereo vision and neural network processing. The system uses passive stereo image pairs and learns to infer depth and geometric features through software-based disparity mapping, substituting mechanical sensing systems with information processing while achieving comparable depth accuracy for manipulation tasks.
Solution Approach 2:
The patent introduces an intermediary neural network processing layer that bridges stereo image input and 3D object manipulation output. This intermediary learns to extract geometric features and generate disparity maps from synthetic training data, serving as a computational mediator that enables accurate depth perception without requiring active depth sensing hardware, thus reducing system complexity.
Data Source
AI summary
A method for training a neural network to perform 3D object manipulation is described. The method includes extracting features from each image of a synthetic stereo pair of images. The method also includes generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images. The method further includes generating, by the neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images. The method also includes manipulating an unknown object perceived from the feature map according to a perception prediction from a prediction head.