Monocular Depth Estimation for AR VR Occlusion Handling
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Solution Overview
Problem
Conventional mono-view depth estimation methods struggle to provide high-quality depth information, especially in occluded regions and fail to accurately handle internal camera parameters, leading to incomplete scene reconstruction for AR and VR applications.
Innovation Solution
A system employing a monocular CNN-based depth estimation module that predicts depth using a single image, incorporates internal camera parameters, and includes edge alignment and panoptic segmentation units to refine depth maps, while a semantic edge detector performs inpainting to add texture and depth information to occluded areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mono-view depth estimation methods are used, then the system complexity is low, but the depth estimation quality is insufficient especially in occluded regions
Solution Approach 1:
The patent segments the depth estimation process into multiple specialized modules: CNN-based depth estimation module for initial depth prediction, edge alignment module for boundary refinement, panoptic segmentation module for object-level understanding, and inpainting module for occluded region completion. Each module handles specific aspects of the problem, improving overall depth estimation quality without requiring a complete system redesign.
Solution Approach 2:
The patent introduces intermediate processing steps between image input and final depth output. The edge alignment module acts as an intermediary to refine depth boundaries, the panoptic segmentation module provides intermediate object labels, and the inpainting module fills gaps in occluded regions. These intermediary components progressively enhance depth estimation quality while maintaining manageable system complexity through modular architecture.
2Productivity
If a single image is used for depth prediction, then the processing speed is fast, but the depth information in occluded regions is incomplete
Solution Approach 1:
The patent performs preliminary depth estimation using the CNN module on the single input image before addressing occluded regions. The edge alignment module then preliminarily refines depth boundaries based on detected edges. Finally, the inpainting module completes the depth information in occluded regions by synthesizing plausible depth values from surrounding areas and semantic context, thereby recovering lost information while maintaining fast single-image processing.
Solution Approach 2:
The inpainting module enables the system to self-complete missing depth information in occluded regions by leveraging semantic segmentation results and edge alignment data. The system uses its own intermediate processing outputs to generate plausible depth values for previously incomplete regions, reducing information loss without requiring additional input images or external data sources.
3Manufacturing precision
If internal camera parameters are not incorporated, then the system is simpler to implement, but the accuracy of three-dimensional reconstruction is reduced
Solution Approach 1:
The patent incorporates internal camera parameters (focal length, principal point, crop size) as configurable inputs to the CNN-based depth estimation module. These parameters are integrated into the network's prediction process, allowing the system to adapt to different camera configurations. The edge alignment and panoptic segmentation modules also utilize these parameters for accurate boundary detection and object segmentation, thereby improving 3D reconstruction accuracy while maintaining relatively simple implementation through parameter passing rather than complex hardware modifications.
Data Source
AI summary
The present invention discloses a system and a method for providing multi-channel high-quality depth estimation from a monocular camera for providing augmented reality (AR) and virtual reality (VR) features to an image. The invention further includes the method to enhance generalization on deployment-friendly monocular depth inference pipeline with semantic information. Furthermore, a vivid and intact reconstruction is guaranteed by inpainting the missing depth and context within the single image input.