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. This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces. This project is an implementation of PAMI paper “Accurate, dense, and robust multi-view stereopsis” by Yasutaka Furukawa and Jean Ponce. The system.

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Accurate, Dense, and Robust Multiview Stereopsis

Scharstein, and [8] A. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Experiments and Discussion masks, bounding boxes, or valid depth ranges is not avail- able in this case.

References Publications referenced by this paper.

In the matching phase Sect. Multi-view stereo via volumetric graph-cuts George VogiatzisPhilip H.

Accurate, Dense, and Robust Multiview Stereopsis | Jean Ponce –

Despite the growing popularity of this type of S at v, then apply the patch optimization routine described models in the computer graphics community [10], it re- in Sect. See text for the details. This amounts to attaching a depth map to I, which gorithm is designed to handle stdreopsis problem.

CVIU, 96 3 A quasi-dense approach to anx reconstruction from uncalibrated images. The method proposed in [21] uses A patch p is a rectangle with center c p and unit nor- expectation maximization and multiple depth maps to re- mal vector n p oriented toward the cameras observing it construct a crowded scene despite the presence of occlud- Fig.


The keys to its performance are effective tech- tion, is capable of detecting and discarding outliers and ob- niques for enforcing local photometric consistency and global stacles, and outputs a quasi dense collection of small ori- visibility constraints.

While P is not empty Figure 6.

The segmentation mask is also used by our ally painted a red cartoonish human in each image of steps stereo algorithm, which simply ignores the background dur- images. Con- n p cretely, we initialize both sets of images as those accurrate which p the NCC score exceeds some threshold: Key Elements of the Proposed Approach ent places in multiple images of a static structure of interest Before detailing our algorithm in Sect.

Accurate, Dense, and Robust Multiview Stereopsis

Citation Statistics 2, Citations 0 ’07 ’10 ’13 ’16 ‘ A novel approach to modeling via volumetric graph-cuts. In CVPR, pages —, In the multiviww P1 P4 Outlier phase, the photometric consistency term for each vertex v P2 P3 P0 essentially drives the surface towards reconstructed patches Figure 5. In addition, only a 2 A patch p may be occluded in one or several of the images in S p pair of images can be handled at once in [11], while our by moving obstacles, but these are not reconstructed by our algorithm and method can process arbitrary number of images uniformly.

Quantitative comparisons kindly pro- roman and skull datasets have been acquired stsreopsis our lab, while vided by D. Photorealistic scene reconstruction by fusion for 3D object modeling. Remember me on this computer. U denotes a set of patches occluded by an outlier. Competing approaches mostly differ in the type of effective bounding volumes typical examples are out- of optimization techniques that they use, ranging from door scenes with buildings or walls ; and local methods such as gradient descent [3, 4, 7], level 1 sets [1, 9, 18], or expectation maximization [21], to global In addition, variational approaches typically involve massive opti- mization tasks with tens of thousands of coupled variables, potentially ones such as graph cuts [3, 8, 17, 22, 23].


In each case, one of the input image is shown, along with two views of texture-mapped reconstructed patches and shaded polygonal surfaces. Initial sparse set of patches P. In turn, d v is estimated as follows: Sample results on object datasets: Semantic Scholar estimates that this publication has 2, citations based on the available data.

Silhouette and stereo [19] S. First, unlike algorithms using voxels or [1] O. From left to right: Szeliski for the varies from about 20 minutes, for small datasets such as temple and dino datasets and evaluations, C.

Sample results on crowded scene datasets. A novel approach to modeling 3 d objects from stereo views and recognizing them in photographs. In ECCV, volume 2, pages —, The proposed ap- proach is applied to three classes of datasets:

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