Sparse-Depth Completion for Mobile 3D Reconstruction
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Solution Overview
Problem
Existing 3D-reconstruction methods, particularly those based on multi-view geometry and RGB-D cameras, face challenges with high computational complexity, power consumption, and the need for multiple-view image data, making them impractical for mobile devices.
Innovation Solution
A 3D-reconstruction technique using a sparse-depth completion network that processes single-view color and depth data to generate a refined dense-depth map, leveraging multi-affinity matrices and guided filtering to enhance depth estimation accuracy while reducing computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-view geometry or RGB-D camera methods are used for 3D reconstruction, then reconstruction accuracy is improved, but computational complexity and power consumption increase significantly
Solution Approach 1:
The patent extracts and utilizes only the essential depth information from sparse depth maps rather than processing complete multi-view image data or full RGB-D streams. By focusing on extracting key depth features and using them to guide the completion network, the system achieves accurate 3D reconstruction while significantly reducing computational complexity and power consumption suitable for mobile devices.
Solution Approach 2:
The patent introduces a sparse-depth completion network as an intermediary component that bridges the gap between sparse depth maps and dense depth maps. This network processes only the essential sparse depth information and generates refined dense depth maps, serving as a mediator that reduces the computational burden of subsequent 3D reconstruction operations while maintaining reconstruction accuracy.
2Measurement precision
If multi-view image data is collected for 3D reconstruction, then reconstruction quality is improved, but data processing time and power consumption increase
Solution Approach 1:
The patent extracts only the necessary depth information from sparse depth maps obtained through single-view imaging, eliminating the need to collect and process multiple views. This extraction approach maintains reconstruction quality by focusing on essential depth features while dramatically reducing data collection and processing time.
Solution Approach 2:
The patent performs preliminary depth completion processing on sparse depth maps before 3D reconstruction. The sparse-depth completion network pre-processes the sparse depth information to generate refined dense depth maps in advance, which then serve as input for the 3D reconstruction pipeline, reducing the overall processing time required for high-quality reconstruction.
3Measurement precision
If dense-depth maps are generated through traditional methods, then depth estimation accuracy is improved, but power consumption increases
Solution Approach 1:
The patent replaces traditional computational mechanics for dense depth map generation with a learned sparse-depth completion network. This neural network-based approach substitutes conventional iterative optimization methods, achieving comparable or superior depth estimation accuracy while consuming significantly less power on mobile devices.
Solution Approach 2:
The patent changes the operational parameters of the depth completion process by using a trained sparse-depth completion network with optimized architecture and parameters. The network is specifically designed and trained to operate efficiently on mobile hardware, achieving high depth estimation accuracy while adapting to the power and performance constraints of mobile platforms.
Data Source
AI summary
A method of video processing is provided. The method may include inputting attribute data and a sparse-depth map associated with an image area into a sparse-depth completion network. The method may include generating a refined dense-depth map based on the attribute data and the sparse-depth map using the sparse-depth completion network. The method may include performing a three-dimensional (3D) reconstruction procedure based on the refined dense-depth map to generate a point cloud of the image area. The method may include performing a triangular-meshing procedure to generate a mesh model based on the point cloud of the image area. The method may include performing a texture-mapping procedure based on the mesh model and the attribute data to generate a textured mesh of the image area. The method may include performing a vertex-normal procedure based on the textured mesh to generate a 3D representation of the image area.