Diffusion Vision System for Noise-Free Object Pose Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current volumetric sensors can report scene geometry and brightness but are unable to effectively use illumination information to refine the location and orientation of objects, leading to limitations in accuracy and noise removal in depth measurement.
Innovation Solution
A diffusion vision-based method and system that combines geometric information from 3D sensors with brightness information from 2D sensors to convert brightness into albedo information, using a machine-learning model to remove sensor noise and refine object pose estimation by uniformly illuminating a target surface with near-infrared light and applying a diffusion-based model to remove noise from geometric and albedo measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If volumetric sensors are used to capture scene geometry and brightness, then the ability to detect object position and orientation is improved, but measurement noise and inaccuracies in depth data increase
Solution Approach 1:
A machine learning model acts as an intermediary between the noisy volumetric sensor data and the final pose estimation. The model processes the raw geometric and brightness information, filtering out measurement noise while extracting meaningful object position and orientation data. This intermediary processing layer transforms imperfect sensor readings into accurate pose estimates without requiring changes to the physical sensor system.
Solution Approach 2:
The system combines multiple types of data (geometric information and brightness information) into a composite data representation that is more robust to noise. By fusing these different data modalities through machine learning, the system creates a more reliable estimate of object pose that compensates for the weaknesses of individual measurement types.
2Measurement precision
If illumination information is used to refine object location and orientation, then pose estimation accuracy is improved, but the complexity of processing multiple data types increases
Solution Approach 1:
The system merges geometric information and brightness information into a unified processing framework. Rather than handling illumination data separately, the machine learning model integrates multiple data types into a single coherent representation, simplifying the overall processing architecture while improving measurement accuracy.
Solution Approach 2:
The machine learning model transforms raw sensor measurements into refined parameters through learned transformations. By changing the representation of the data from raw sensor values to processed features, the system extracts meaningful information while reducing noise and complexity in subsequent processing stages.
3Measurement precision
If a machine-learning model is applied to remove sensor noise, then measurement accuracy is improved, but computational processing time increases
Solution Approach 1:
The machine learning model performs preliminary processing of the sensor data, identifying and correcting noise patterns before subsequent pose estimation steps. By addressing measurement errors early in the processing pipeline, the system prevents noise from propagating through later stages, reducing the need for iterative corrections and saving overall processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the creation of high-fidelity depictions of scenes by removing depth measurement noise and improving the accuracy of object pose estimation, making it invariant to rotation, position, and ambient light conditions.
Implementation Method 1
sensing, via a volumetric sensor, brightness of a target surface due to a diffuse component of backscattered illumination
Data Source
AI summary
A method and system for creating a high-fidelity depiction of a scene including an object within the scene are provided. The method includes uniformly illuminating a target surface of the object with light to obtain reflected, backscattered illumination. The method also includes sensing via a volumetric sensor, brightness of the surface due to a diffuse component of the backscattered illumination to obtain brightness information. Backscattered illumination from the target surface is inspected to obtain geometric measurements which include sensor noise. Rotationally and positionally invariant measured surface albedo including albedo noise of the object is computed based on the brightness and the geometric measurements. A machine-learning model such as a diffusion sensor model is applied to the geometric measurements and the measured surface albedo to remove the sensor noise and the albedo noise, respectively, to obtain a prediction of actual geometry and actual albedo, respectively, of the object.