Depth Sensor Point Cloud Completion Using Multiview Cameras
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Solution Overview
Problem
Existing mediated reality systems face challenges in capturing complete and accurate depth information due to occluded surfaces and complex geometries, leading to incomplete and inaccurate reconstructions of three-dimensional environments.
Innovation Solution
A method that combines depth data from a depth sensor with image data from multiple cameras to generate a merged point cloud, filling in missing regions with image-based depth data to enhance accuracy and resolution, while maintaining real-time processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dedicated depth sensor is used to capture depth information, then depth data can be obtained, but complete and accurate depth information is lost due to occluded surfaces and complex geometries
Solution Approach 1:
The patent combines depth data from a depth sensor with image data from multiple cameras to create a merged point cloud. This merging allows the system to leverage both the broad coverage of depth sensors and the occlusion-penetrating capability of multi-view camera systems, filling in missing regions where the depth sensor failed to capture data.
Solution Approach 2:
The patent uses image data from multiple cameras as an intermediary to recover depth information in regions where the depth sensor failed. By processing images from different camera angles and synthesizing depth maps, the system acts as a mediator to fill gaps in the depth data caused by occlusions and complex geometries.
2Loss of information
If multiple cameras are used to capture image data for depth reconstruction, then occluded areas can be covered, but processing time increases
Solution Approach 1:
The patent applies partial action by focusing computational resources only on regions where the depth sensor failed to capture data. Instead of processing all image data from multiple cameras uniformly, the system identifies missing regions in the depth map and selectively processes only those areas using multi-view stereo algorithms, reducing overall processing time while maintaining completeness.
3Measurement precision
If depth data from multiple sources is combined, then accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the depth reconstruction process into distinct modules: a depth sensing module, an image capture module, a point cloud generation module, and a merging module. Each module handles a specific aspect of the process, allowing for independent optimization and simplifying the overall system architecture despite the multi-source data integration.
Data Source
AI summary
Methods of determining the depth of a scene and associated systems are disclosed herein. In some embodiments, a method can include augmenting depth data of a scene captured with a depth sensor with depth data from one or more images of the scene. For example, the method can include capturing image data of the scene with a plurality of cameras. The method can further include generating a point cloud representative of the scene based on the depth data from the depth sensor and identifying a missing region of the point cloud, such as a region occluded from the view of the depth sensor. The method can then include generating depth data for the missing region based on the image data. Finally, the depth data for the missing region can be merged with the depth data from the depth sensor to generate a merged point cloud representative of the scene.