ACCURATE DENSE AND ROBUST MULTIVIEW STEREOPSIS PDF

Step 1. Initial Match 1. Currently, for convenience, this part is implemented with OpenCV Version 2. And only the features 1 fall on the epipolar line and 2 with the same type Harris or DoG as the reference feature wil be considered as matching candidates. For the epipolar geometry calculation, I tried to use OpenCV at first, but the results produce by OpenCV triangulation methods seems quite problematic.

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Step 1. Initial Match 1. Currently, for convenience, this part is implemented with OpenCV Version 2. And only the features 1 fall on the epipolar line and 2 with the same type Harris or DoG as the reference feature wil be considered as matching candidates. For the epipolar geometry calculation, I tried to use OpenCV at first, but the results produce by OpenCV triangulation methods seems quite problematic.

So I had to create my own implementation, and found the bible book "Multiple view geometry in computer vision" an excellent reference for implementing this. For this part, I generally follows the algorithm below. Except for the Patch Optimization Refine part, while the conjugate gradient method was applied by the paper using Wnlib library, I met some problems when compiling Wnlib under Windows.

So as a temporary solution, I applied a simple searching method iteratively search for minimum point in the nearby value space instead, which I think although slower, but should have the same effect as conjugate gradient method.

Step 2. Expansion As the initial match has only a sparse set of patches, expansion is important to produce patches dense enough for reconstruction. In this stage, I followed the algorithm below. Step 3. Filtering Currently filtering is the most problematic part of the system, some outliers are not filtered out properly. A big improvement is expected to be done. Known Issues and Future Work As the project is still on going, the tasks below are to be finished: 1. The filtering issue mentioned above.

Polygonal mesh reconstruction. Performance improvement. This implementation runs over an hour to produce patches, while official PMVS needs less than 20 minutes. I am not sure 3 Some calculation duplication in my implementation 4.

Currently the 3D views are rendered using PatchViewer provided by PMVS, as a customized viewer will provide more convenience for debugging and testing, I plan to implement my own PatchViewer in next step.

References: 1. Furukawa Y, Ponce J. Accurate, dense, and robust multiview stereopsis[J]. Hartley and A. Cambridge University Press,

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Accurate, dense, and robust multiview stereopsis.

Resources and Help Accurate, Dense, and Robust Multiview Stereopsis Abstract: This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges.

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Patch-based Multi View Stereo

Accurate, dense, and robust multiview stereopsis. Furukawa Y 1 , Ponce J. Author information: 1 Google Inc. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints.

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Accurate, Dense, and Robust Multiview Stereopsis

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