Gradient Depth Reconstruction with Alpha Masking
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Solution Overview
Problem
Existing methods for estimating depth in 3D modeling from 2D images face challenges such as specular highlights and object locomotion, which introduce errors in the depth map generation and refinement process.
Innovation Solution
The method involves warping target images, determining alpha masks with per-pixel weights, and updating the depth map using these masks to reduce the impact of errors like specular highlights and locomotion, while also applying interpolation grids and derivatives to refine the depth map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard depth estimation methods are used from 2D images, then depth maps can be generated, but errors from specular highlights and object locomotion significantly degrade the accuracy of the depth map
Solution Approach 1:
The patent segments the image processing by creating multiple candidate depth maps from different image patches, then uses gradient analysis to selectively combine reliable patches while discarding unreliable ones (such as those affected by specular highlights or locomotion). This segmentation approach allows the system to process only reliable portions of the image data.
Solution Approach 2:
The patent introduces gradient magnitude calculations and alpha mask computation as intermediary steps between raw image data and final depth maps. These intermediaries serve as filters that identify and eliminate erroneous depth information from specular highlights and locomotion artifacts before the final depth map is constructed.
2Measurement precision
If multiple images are captured to improve depth estimation, then more data is available for refinement, but the complexity of processing and aligning multiple images increases
Solution Approach 1:
The patent divides the multiple captured images into separate processing streams, where each image is independently processed to generate candidate depth maps. This segmentation allows parallel processing of individual images while maintaining the ability to compare and combine results, reducing the computational burden compared to processing all images as a single complex dataset.
Solution Approach 2:
The patent creates multiple copies of the depth estimation process, generating candidate depth maps from different images. These copies are then compared and synthesized to produce the final depth map, allowing the system to leverage multiple images without requiring a completely new processing architecture for each additional image.
3Manufacturing precision
If traditional mesh generation methods are used, then a basic 3D model can be created, but errors in the depth map propagate through to the point cloud and mesh, resulting in a poor quality 3D model
Solution Approach 1:
The patent performs preliminary depth map refinement and error filtering before the mesh generation process begins. By pre-processing the depth maps to remove errors from specular highlights and locomotion, the system ensures that the subsequent mesh generation operates on clean, accurate data, preventing error propagation to the final 3D model.
Solution Approach 2:
The patent implements a feedback loop where candidate depth maps are generated, evaluated for reliability using gradient analysis, and then used to update and refine the final depth map. This iterative feedback process continuously improves depth map accuracy before the data is passed to mesh generation, ensuring high-quality 3D model output.
Data Source
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AI summary
A method of generating a refined depth map for a three-dimensional (3D) model of an object is described. The method may include may include warping a set of target images, resulting in warped target images, wherein the set of target images is selected from the plurality of images; determining a difference between each warped target image and a reference image, wherein the reference image is from the plurality of images; determining, for each warped target image, an alpha mask comprising per-pixel weights; and updating an initialized depth map based on alpha mask, resulting in the estimated depth map. Related systems, devices and computer program products are also described.