Mixed Reality Pass-Through View Tracker Distortion Removal

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Solution Overview

Problem

In reality services, particularly in mixed reality (MR) scenarios, trackers such as handheld controllers or wearable devices can distort the pass-through view due to their presence within the field of view, negatively affecting the visual quality of the rendered image.

Innovation Solution

A method for generating a pass-through view based on a tracker status, involving the determination of a target depth map using a predetermined depth map or a first depth map associated with the field of view and tracker information, and rendering the pass-through view using an image and camera parameters to mitigate distortion.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the HMD renders the pass-through view including the tracker in the field of view, then the user can see the real world scene with the tracker, but the image region near the tracker becomes distorted and visual quality deteriorates

Engineering Contradiction:
Improvevisual quality of pass-through viewVSAvoiddistortion caused by tracker
Core Design Contradiction:
ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The patent extracts the tracker from the pass-through view by determining its position and depth, then excludes it from the rendering process. The host device identifies the tracker in the captured image, calculates its spatial location using depth information, and removes it from the final rendered view, preventing distortion while maintaining the rest of the real-world scene.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The patent changes the depth parameter of the tracker to exclude it from the pass-through view. By modifying the depth value associated with the tracker's position, the system effectively moves the tracker outside the visible range or adjusts its rendering priority, thereby eliminating the distortion effect without affecting other scene elements.

Inventive Principle:
Principle #35Parameter changes

2Reliability

If the HMD excludes the tracker from the pass-through view, then distortion is reduced, but the user loses awareness of the tracker's position in the real world

Engineering Contradiction:
Improvevisual quality of pass-through viewVSAvoidtracker position information
Core Design Contradiction:
ReliabilityVSLoss of information

Solution Approach 1:

The patent segments the pass-through view into multiple regions: the tracker region (excluded from rendering to prevent distortion) and the background scene region (rendered normally). By dividing the view in this manner, the system can selectively process different areas with different requirements, maintaining visual quality while preserving spatial awareness through the overall scene composition.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent uses depth map information as an intermediary to mediate between the tracker's physical presence and its visual representation. The depth data serves as a bridge that allows the system to accurately determine tracker position and apply appropriate rendering decisions, ensuring both distortion reduction and spatial awareness without direct conflict.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentUS20240273689A1Method for generating pass-through view according to tracker status and host
Publication Date: 2024.08.15 HTC CORP
  • US20240273689A1 patent drawing
  • US20240273689A1 patent drawing
  • US20240273689A1 patent drawing

AI summary

The embodiments of the disclosure provide a method for generating a pass-through view with better scale and a host. The method includes: in response to determining that the tracker status of a tracker satisfies a predetermined condition, generating a target depth map based on a predetermined depth map or a first depth map associated with a field of view (FOV) of the host and a tracker information associated with the tracker; and rendering the pass-through view based on an image associated with the FOV of the host, a camera parameter, and the target depth map.