Egocentric 3D Point Cloud Pose Estimation for Real-Time Accuracy
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Solution Overview
Problem
Conventional RGB-based pose estimation methods are susceptible to lighting variations, clothing colors, and self-occlusion, requiring large amounts of high-quality training data and complex architectures that hinder real-time processing in egocentric perspectives.
Innovation Solution
A method using 3D point cloud data from an egocentric perspective, involving preprocessing, grid-based sampling, and a lightweight neural network for pose estimation, with automatic generation of pose ground-truth data through iterative refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RGB image-based pose estimation is used, then training data requirements increase and computational complexity increases, but pose estimation accuracy decreases due to sensitivity to lighting, clothing colors, and occlusion
Solution Approach 1:
The patent replaces RGB image processing with 3D point cloud data processing. Instead of using complex neural networks to analyze images susceptible to lighting and color variations, the system uses depth sensor data that directly captures spatial information, substituting the mechanical/optical processing system with a geometric data processing approach that is inherently more robust to environmental factors
Solution Approach 2:
The patent changes the fundamental data representation from 2D RGB images to 3D point cloud data. This parameter change transforms the input from image pixels (which are sensitive to lighting and color) to spatial coordinates (which are invariant to these factors), fundamentally altering how pose estimation is performed and reducing computational complexity
2Ease of operation
If egocentric perspective is used, then user mobility constraints are reduced and real-time responsiveness is improved, but self-occlusion increases and training data collection becomes more difficult
Solution Approach 1:
The patent introduces 3D point cloud data as an intermediary representation that bridges the egocentric camera view and the target pose estimation. The point cloud serves as a mediator that captures spatial information from the limited egocentric field of view while providing a structured representation that can be processed to estimate poses even when parts of the body are occluded
Solution Approach 2:
The patent transitions from 2D image data to 3D point cloud data, adding the depth dimension. This dimensional change enables the system to represent spatial relationships and body geometry in three dimensions, which helps overcome self-occlusion problems by providing depth information that allows inference of occluded body parts
3Reliability
If conventional deep learning architecture is used, then pose estimation can be performed, but real-time processing becomes difficult due to high computational complexity
Solution Approach 1:
The patent extracts only the essential spatial information from the 3D point cloud data through grid-based sampling and feature map generation. By extracting and representing only the critical geometric features needed for pose estimation, the system reduces the computational burden while maintaining estimation accuracy, enabling real-time processing
Data Source
AI summary
The present disclosure relates to a technique for real-time pose estimation of a user based on egocentric 3D point cloud data. According to one aspect of the present disclosure, a method is provided for performing real-time pose prediction through preprocessing of point cloud data, grid-based sampling, feature map transformation, and a lightweight neural network-based pose estimation model. Additionally, for training the pose estimation model, the present disclosure provides a method for automatically generating pose ground-truth data by aligning coordinate systems between a fixed external motion sensor and a depth sensor worn by the user, and for constructing a reliable training dataset through iterative refinement.