Proves a theorem for 3D Poincaré duality pairs.
arXiv research
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Analyze and predict complex 3D shape deformations using LSTM autoencoders and oriented bounding boxes.
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the …
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR. In this paper we build upon recently introduced 3D mesh-convolutional Variational AutoEncoders which have shown great promise for learning rich representations of deformab…
The common assertion that the Ricci flows of Einstein spaces with cosmological constant can be modelled by certain classes of nonholonomic frame, metric and linear connection deformations resulting in nonhomogeneous Einstein spaces is examined in the light of the role played by topological three dimensional (3D) Taub-N…
The paper connects quivers to knot complements and studies their BPS states and 3d N=2 theories.
Paper develops a new method to analyze 3D tree-like objects.
The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving. One commonly used 3D data type is 3D point clouds, which describe shape information. We examine the problem of creating robust models from the …
The study characterizes and proves properties of 3D Poisson quasi-Nijenhuis manifolds.
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to …
Differentiable voxelization for 3D meshes with GPU acceleration.
Cardiac motion modeling using LDDMM and shape splines.
Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determin…
Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perception from natural images have focused on low-dimensional latent scenes, due to challenges in both modeling and inference. Accounting for th…
A novel 3D shape registration method using spectral graph embedding and probabilistic matching.
The only known example of collapsed three-dimensional complete gradient steady Ricci solitons so far is the 3D cigar soliton , the product of Hamilton's cigar soliton and the real line with the product metric. R. Hamilton has conjectured that there should exist a family of colla…
Unsupervised mesh disentanglement separates identity and pose.
Researchers map knot complements using 3d theories and half-index calculations.
The study investigates deformations of swallowtails in 3D space, preserving curvature signs.
New proof shows 3D shapes can be continuously deformed.
One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations. Starting from the recent idea of viewpoint factorization, we propose a new app…
The paper generalizes knot invariants and their connections to quivers and ideals.
Explains a 2D color exchange invariant correspondence to 3D linking numbers.
The paper studies rigidity of sphere packings on 3D manifolds with boundary.
New shape representation for airfoils improves design and manufacturing.
CycleMorph improves image registration by preserving topology with cycle consistency.
Quasi-conformal (QC) theory is an important topic in complex analysis, which studies geometric patterns of deformations between shapes. Recently, computational QC geometry has been developed and has made significant contributions to medical imaging, computer graphics and computer vision. Existing computational QC theor…
Compactification of M- / string theory on manifolds with structure yields a wide variety of 4D and 3D physical theories. We analyze the local geometry of such compactifications as captured by a gauge theory obtained from a three-manifold of ADE singularities. Generic gauge theory solutions include a non-trivial g…
We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry. Recent works have developed models that exploit the intrinsic geometry of manifolds and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants, which learn…
The paper studies rigid sphere packings on 3D manifolds with boundary.
We review a construction of a new class of algebraic curves, called super-A-polynomials, and their quantum generalizations. The super-A-polynomial is a two-parameter deformation of the A-polynomial known from knot theory or Chern-Simons theory with SL(2,C) gauge group. The two parameters of the super-A-polynomial encod…
New quantum algebra connects 3D gravity to complex plane.
3D dual field theories for Virasoro minimal models constructed using Seifert fiber spaces.
LIMP learns latent shapes with metric preservation, improving generative models.
We introduce and compute a 2-parameter family deformation of the A-polynomial that encodes the color dependence of the superpolynomial and that, in suitable limits, reduces to various deformations of the A-polynomial studied in the literature. These special limits include the t-deformation which leads to the "refined A…
In this paper we examine a new class of five dimensional (5D) exact solutions in extra dimension gravity possessing Lie algebroid symmetry. The constructions provide a motivation for the theory of Clifford nonholonomic algebroids elaborated in Ref. hep-th/0501217. Such Einstein-Dirac spacetimes are parametrized by gene…
DreamFusion uses text-to-image diffusion models to create 3D images efficiently.
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolut…
Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for challenging tasks including 3D points generation, reconstruction, compression…
Single-particle electron cryomicroscopy is an essential tool for high-resolution 3D reconstruction of proteins and other biological macromolecules. An important challenge in cryo-EM is the reconstruction of non-rigid molecules with parts that move and deform. Traditional reconstruction methods fail in these cases, resu…
Generates coherent 3D scenes from monocular videos without supervision.
GCDM generates valid large 3D molecules and optimizes existing molecules.
3D object detection improved using energy-based models.
Equivariant diffusion model generates 3D molecules efficiently.
3D Axial-Attention improves lung nodule classification accuracy.
ROOTS learns to represent and render 3D scenes with object-centric models.
GIBLy adds geometric priors to 3D segmentation models, improving performance with minimal overhead.