Multi-frame 3D Reconstruction via Weighted Depth Integration
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Solution Overview
Problem
Existing systems for constructing 3D models are costly and cannot satisfy the demand for 3D content in applications like computer graphics, virtual reality, and communications.
Innovation Solution
A method and apparatus for multi-frame three-dimensional reconstruction (3DR) that projects sample locations onto multiple depth frames, reads depth prediction values, obtains 3D points, generates weighted depth prediction values, and updates sample locations to enhance 3DR quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized hardware systems are used for 3D reconstruction, then 3D model construction capability is improved, but system cost increases
Solution Approach 1:
The patent uses multiple 2D image frames as copies of the 3D scene from different viewpoints to reconstruct the 3D model, replacing the need for specialized 3D sensing hardware. By treating 2D images as sufficient inputs for 3D reconstruction, the system achieves reliable 3D modeling using only standard cameras, thereby reducing system cost while maintaining construction capability.
Solution Approach 2:
The patent replaces specialized hardware systems with a computational image processing approach. Instead of using dedicated 3D reconstruction hardware, the system uses software-based multi-frame processing algorithms that analyze 2D images to infer 3D structure, substituting mechanical/specialized hardware with optical-computational methods.
2Productivity
If single-frame depth prediction is used, then processing speed is improved, but reconstruction quality deteriorates
Solution Approach 1:
The patent merges information from multiple depth frames by projecting sample locations onto several frames and combining their depth predictions. This integration of multiple frames allows the system to maintain processing efficiency while improving reconstruction quality through aggregated depth information, resolving the trade-off between speed and quality.
Solution Approach 2:
The patent implements a feedback mechanism where depth predictions from multiple frames are used to refine and update the 3D reconstruction iteratively. By incorporating feedback from multiple viewpoint predictions, the system improves measurement precision while maintaining acceptable processing speeds through efficient feedback integration.
3Measurement precision
If multiple depth frames are processed, then reconstruction quality is improved, but computational complexity increases
Solution Approach 1:
The patent segments the 3D space into discrete sample locations within volumetric blocks, allowing independent processing of each location across multiple frames. This segmentation enables the system to handle multiple depth frames by processing individual sample points separately, reducing computational complexity while maintaining reconstruction quality through systematic coverage of the 3D space.
Solution Approach 2:
The patent applies local quality processing by determining depth values for specific sample locations within blocks rather than processing entire frames uniformly. By focusing computational resources on local sample points and using viewpoint-dependent processing, the system achieves high reconstruction quality while managing computational complexity through localized rather than global processing.
Data Source
AI summary
Disclosed are systems and techniques for image processing. For example, a computing device can project a sample location (of a plurality of sample locations) of a block of a scene onto depth frames to determine pixel values for the sample location. Each of the depth frames corresponds to a pose and includes a depth prediction value corresponding to the sample location. The computing device can read the depth prediction values of the depth frames at the pixel values for the sample location on each depth frame of the depth frames. The computing device can obtain 3D points of the depth prediction values in a three-dimensional (3D) space. The computing device can then generate weighted depth prediction values by assigning a weight to each depth prediction value of the depth prediction values. The computing device can update the sample location based on the weighted depth prediction values.