7 research outputs found
Semantic 3D Reconstruction with Finite Element Bases
We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Starting from the popular formulation of labeling as a convex relaxation
by functional lifting, we show that FEM discretisation is valid for the most
general case, where the regulariser is anisotropic and non-metric. While our
findings are generic and applicable to different vision problems, we
demonstrate their practical implementation in the context of semantic 3D
reconstruction, where such regularisers have proved particularly beneficial.
The proposed FEM approach leads to a smaller memory footprint as well as faster
computation, and it constitutes a very simple way to enable variable, adaptive
resolution within the same model
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
Recommended from our members
Cardiac computed tomographic imaging in cardio-oncology: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT). Endorsed by the International Cardio-Oncology Society (ICOS).
Cardio-Oncology is a rapidly growing sub-specialty of medicine, however, there is very limited guidance on the use of cardiac CT (CCT) in the care of Cardio-Oncology patients. In order to fill in the existing gaps, this Expert Consensus statement comprised of a multidisciplinary collaboration of experts in Cardiology, Radiology, Cardiovascular Multimodality Imaging, Cardio-Oncology, Oncology and Radiation Oncology aims to summarize current evidence for CCT applications in Cardio-Oncology and provide practice recommendations for clinicians