Point Cloud Noise Removal via Multi-View Consistency
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Solution Overview
Problem
Existing image-based 3D reconstruction techniques often produce noisy point clouds due to matching ambiguities and image imperfections, leading to incorrect geometry and loss of fine details when attempting to remove outliers and noise.
Innovation Solution
The proposed method enhances noise and outlier removal by evaluating geometric and photometric consistency of points across multiple views, allowing for more accurate and detailed 3D model reconstruction without requiring extensive smoothing or regularization, thereby improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strong smoothing or regularization is applied in depth computation to reduce outliers, then noise and outliers are reduced, but fine details are destroyed and computational cost increases
Solution Approach 1:
The patent segments the point cloud processing into two distinct stages: first computing depth maps with minimal regularization to preserve details, then separately removing outliers from the resulting point cloud. This segmentation allows each stage to optimize for its specific goal without compromising the other.
Solution Approach 2:
The patent performs preliminary depth map computation with little to no regularization before outlier removal. By computing depth maps first with minimal smoothing, the fine details are preserved in the initial point cloud, and then outliers are removed in a subsequent step without having to apply strong regularization that would have destroyed the details.
2Reliability
If strong smoothing or regularization is applied in depth computation to reduce outliers, then noise and outliers are reduced, but computational cost increases
Solution Approach 1:
The patent segments the processing into two independent stages: depth map computation with minimal regularization, followed by separate outlier removal. This avoids the computational burden of solving large global optimization problems with strong regularization, while still achieving effective outlier reduction in the second stage.
Solution Approach 2:
The patent uses a lightweight, computationally inexpensive depth estimation approach that does not require expensive iterative optimization with strong regularization. The method accepts that the initial depth maps may contain outliers, but relies on the subsequent efficient outlier removal step to clean the data, rather than investing heavy computational resources in preventing outliers during depth computation.
3Manufacturing precision
If little to no regularization is applied in depth computation to preserve details, then fine details are preserved, but noise and outliers increase
Solution Approach 1:
The patent explicitly segments the processing into two stages: first computing depth maps with minimal regularization to preserve fine details in the point cloud, then performing a dedicated outlier removal step that uses geometric and photometric consistency checks to identify and remove noisy points without affecting the preserved details.
4Reliability
If conventional outlier removal methods are used, then noise is reduced, but fine details are lost
Solution Approach 1:
The patent changes the parameters used for outlier detection by incorporating both geometric consistency (how well points fit the reconstructed surface) and photometric consistency (how well point colors match across different views). This multi-parameter approach allows for more nuanced outlier identification that preserves fine details while removing noise, unlike conventional methods that rely on simpler criteria.
Data Source
AI summary
Enhanced removing of noise and outliers from one or more point sets generated by image-based 3D reconstruction techniques is provided. In accordance with the disclosure, input images and corresponding depth maps can be used to remove pixels that are geometrically and/or photometrically inconsistent with the colored surface implied by the input images. This allows standard surface reconstruction methods (such as Poisson surface reconstruction) to perform less smoothing and thus achieve higher quality surfaces with more features. In some implementations, the enhanced point-cloud noise removal in accordance with the disclosure can include computing per-view depth maps, and detecting and removing noisy points and outliers from each per-view point cloud by checking if points are consistent with the surface implied by the other input views.