Hand Tracking via Depth Map Smoothing and POV Correction
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Solution Overview
Problem
In extended reality (XR) environments, hand tracking is impaired due to discrepancies between the camera's perspective and the user's point of view, leading to poor distance perception, disorientation, and hand-eye coordination issues.
Innovation Solution
The method involves obtaining uncorrected hand tracking data and a depth map of the physical environment, identifying the position of a finger portion, performing spatial depth smoothing around the finger position, and applying point of view (POV) correction to generate corrected hand tracking data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand tracking data is obtained directly from camera without correction, then processing is simple and fast, but hand tracking accuracy and distance perception are impaired due to perspective discrepancies
Solution Approach 1:
A depth map serves as an intermediary data structure between the raw camera image and the hand tracking correction process. The depth map encodes distance information for each pixel, enabling accurate 3D position calculation of hand features while maintaining a relatively simple processing pipeline. This intermediary representation resolves the contradiction by providing the necessary geometric information without requiring complex direct 3D reconstruction algorithms.
Solution Approach 2:
The patent transitions from 2D image coordinates to 3D spatial coordinates by incorporating depth information from the depth map. This dimensional transformation allows hand tracking to occur in three-dimensional space, accurately representing the actual position and orientation of hand features relative to the camera, thereby improving measurement precision while using standard coordinate transformation techniques.
2Measurement precision
If depth map smoothing is applied around finger regions, then hand tracking precision is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies depth map smoothing selectively only in regions containing finger portions, rather than smoothing the entire depth map. By identifying finger regions through hand tracking data and applying smoothing operations locally to those specific areas, the system improves finger position accuracy while minimizing the computational resources required, as the smoothing operation is performed on a limited portion of the depth map.
Solution Approach 2:
The patent applies smoothing operations with controlled intensity and scope - using a radius parameter to limit the spatial extent of smoothing and applying it only where necessary (in finger regions). This partial application of the smoothing operation achieves sufficient precision improvement for hand tracking while avoiding the excessive computational cost that would result from applying full-strength smoothing to the entire depth map.
Data Source
AI summary
In some implementations, a method includes: obtaining uncorrected hand tracking data; obtaining a depth map associated with a physical environment; identifying a position of a portion of the finger within the physical environment based on the depth map and the uncorrected hand tracking data; performing spatial depth smoothing on a region of the depth map adjacent to the position of the portion of the finger; and generating corrected hand tracking data by performing point of view (POV) correction on the uncorrected hand tracking data based on the spatially depth smoothed region of the depth map adjacent to the portion of the finger.