Object Pose Neural Network Using Heat Maps and Synthetic Data
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Solution Overview
Problem
Existing neural network systems struggle to accurately predict the pose of objects, especially when using low-cost materials with inherent kinematic errors, and require costly human-labeled training data.
Innovation Solution
A neural network system that generates heat maps for feature points and uses a differentiable object pose solver, allowing end-to-end training with intermediate supervision, and generates training data through fiducial markers and kinematic data interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If neural network systems use low-cost materials and sensors, then device complexity and cost are reduced, but measurement precision and reliability deteriorate due to inherent kinematic errors
Solution Approach 1:
The patent introduces heat maps as an intermediary representation between the input image and the final pose prediction. The neural network first generates heat maps indicating likely locations of feature points, then solves for pose from these heat maps. This intermediate step allows the system to handle noisy sensor data from low-cost materials by providing a probabilistic buffer that smooths out kinematic errors before final pose calculation.
Solution Approach 2:
The patent replaces traditional mechanical pose estimation methods (which rely on precise kinematic sensors and materials) with a neural network-based visual system. Instead of using expensive encoders and precision mechanical components, the system uses image processing and learned feature point detection to estimate pose, substituting mechanical precision requirements with computational inference.
2Measurement precision
If neural network systems use human-labeled training data, then training accuracy is improved, but loss of time and training costs increase
Solution Approach 1:
The patent enables the neural network to be self-trained using automatically generated synthetic data. The system generates training examples by rendering images of objects in various poses with known ground truth pose information, eliminating the need for manual human annotation. This self-service approach to data generation dramatically reduces training time and costs while providing sufficient training accuracy through the large volume of automatically created training samples.
3Productivity
If neural network systems implement end-to-end training with intermediate supervision, then productivity and automation are improved, but device complexity increases
Solution Approach 1:
The patent segments the training process into two distinct phases: intermediate supervision during early training using heat map targets, and end-to-end training in later stages using final pose targets. This segmentation allows the system to first learn robust feature point detection through intermediate heat map supervision, then refine overall pose prediction through end-to-end optimization, improving training efficiency while managing complexity through staged learning.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for predicting object pose. In one aspect, a method includes receiving an image of an object having one or more feature points; providing the image as an input to a neural network subsystem trained to receive images of objects and to generate an output including a heat map for each feature point; applying a differentiable transformation on each heat map to generate respective one or more feature coordinates for each feature point; providing the feature coordinates for each feature point as input to an object pose solver configured to compute a predicted object pose for the object, wherein the predicted object pose for the object specifies a position and an orientation of an object; and receiving, at the output of the object pose solver, a predicted object pose for the object in the image.