828 research outputs found
Semantically Informed Multiview Surface Refinement
We present a method to jointly refine the geometry and semantic segmentation
of 3D surface meshes. Our method alternates between updating the shape and the
semantic labels. In the geometry refinement step, the mesh is deformed with
variational energy minimization, such that it simultaneously maximizes
photo-consistency and the compatibility of the semantic segmentations across a
set of calibrated images. Label-specific shape priors account for interactions
between the geometry and the semantic labels in 3D. In the semantic
segmentation step, the labels on the mesh are updated with MRF inference, such
that they are compatible with the semantic segmentations in the input images.
Also, this step includes prior assumptions about the surface shape of different
semantic classes. The priors induce a tight coupling, where semantic
information influences the shape update and vice versa. Specifically, we
introduce priors that favor (i) adaptive smoothing, depending on the class
label; (ii) straightness of class boundaries; and (iii) semantic labels that
are consistent with the surface orientation. The novel mesh-based
reconstruction is evaluated in a series of experiments with real and synthetic
data. We compare both to state-of-the-art, voxel-based semantic 3D
reconstruction, and to purely geometric mesh refinement, and demonstrate that
the proposed scheme yields improved 3D geometry as well as an improved semantic
segmentation
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
-minimization algorithm by adaptively estimating the value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image
processin
Minimizing the Multi-view Stereo Reprojection Error for Triangular Surface Meshes
International audienceThis article proposes a variational multi-view stereo vision method based on meshes for recovering 3D scenes (shape and radiance) from images. Our method is based on generative models and minimizes the reprojection error (difference between the observed images and the images synthesized from the reconstruction). Our contributions are twofold. 1) For the first time, we rigorously compute the gradient of the reprojection error for non smooth surfaces defined by discrete triangular meshes. The gradient correctly takes into account the visibility changes that occur when a surface moves; this forces the contours generated by the reconstructed surface to perfectly match with the apparent contours in the input images. 2) We propose an original modification of the Lambertian model to take into account deviations from the constant brightness assumption without explicitly modelling the reflectance properties of the scene or other photometric phenomena involved by the camera model. Our method is thus able to recover the shape and the diffuse radiance of non Lambertian scenes
Facetwise Mesh Refinement for Multi-View Stereo
Mesh refinement is a fundamental step for accurate Multi-View Stereo. It
modifies the geometry of an initial manifold mesh to minimize the photometric
error induced in a set of camera pairs. This initial mesh is usually the output
of volumetric 3D reconstruction based on min-cut over Delaunay Triangulations.
Such methods produce a significant amount of non-manifold vertices, therefore
they require a vertex split step to explicitly repair them. In this paper, we
extend this method to preemptively fix the non-manifold vertices by reasoning
directly on the Delaunay Triangulation and avoid most vertex splits. The main
contribution of this paper addresses the problem of choosing the camera pairs
adopted by the refinement process. We treat the problem as a mesh labeling
process, where each label corresponds to a camera pair. Differently from the
state-of-the-art methods, which use each camera pair to refine all the visible
parts of the mesh, we choose, for each facet, the best pair that enforces both
the overall visibility and coverage. The refinement step is applied for each
facet using only the camera pair selected. This facetwise refinement helps the
process to be applied in the most evenly way possible.Comment: Accepted as Oral ICPR202
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