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Found 54 papers, 23 papers with code
Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and Correspondences
Instead, we propose a pipeline that can leverage correspondence distributions and shape information from the CAD model, which are later used to learn pose distributions.
UV-free Texture Generation with Denoising and Geodesic Heat Diffusions
Seams, distortions, wasted UV space, vertex-duplication, and varying resolution over the surface are the most prominent issues of the standard UV-based texturing of meshes.
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms
Our experimental results demonstrate that our new complexity measures correlate highly with generalization error in industry-standards architectures such as transformers and deep graph networks.
NeRF-Feat: 6D Object Pose Estimation using Feature Rendering
During inference, CNN is used to predict view-invariant features which can be used to establish correspondences with the implicit 3d model in NeRF.
Attending to Topological Spaces: The Cellular Transformer
Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data.
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge.
HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation
Since hyperbolic space is suitable for handling hierarchical data, we propose to learn the hierarchical representations of text and 3D shapes in hyperbolic space.
Position: Topological Deep Learning is the New Frontier for Relational Learning
At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
Fun with Flags: Robust Principal Directions via Flag Manifolds
In this work, we present a unifying formalism for PCA and its variants, and introduce a framework based on the flags of linear subspaces, ie a hierarchy of nested linear subspaces of increasing dimension, which not only allows for a common implementation but also yields novel variants, not explored previously.
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs
In this context, cell complexes are often seen as a subclass of hypergraphs with additional algebraic structure that can be exploited, e. g., to develop a spectral theory.
SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark
We present SignAvatars, the first large-scale, multi-prompt 3D sign language (SL) motion dataset designed to bridge the communication gap for Deaf and hard-of-hearing individuals.
Generative Fractional Diffusion Models
To ensure tractable inference and learning, we employ a recently popularized Markov approximation of fBM (MA-fBM) and derive its reverse time model, resulting in generative fractional diffusion models (GFDMs).
Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients
We present, QP-SBGD, a novel layer-wise stochastic optimiser tailored towards training neural networks with binary weights, known as binary neural networks (BNNs), on quantum hardware.
Variational Inference for SDEs Driven by Fractional Noise
In this paper, building upon the Markov approximation of fBM, we derive the evidence lower bound essential for efficient variational inference of posterior path measures, drawing from the well-established field of stochastic analysis.
Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs
However, the applicability of such advancements to the video domain has been hindered by the difficulty of representing and controlling videos in the latent space of GANs.
Chordal Averaging on Flag Manifolds and Its Applications
This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median of a set of points on a flag manifold under the chordal metric.
Disentangling Content and Motion for Text-Based Neural Video Manipulation
Giving machines the ability to imagine possible new objects or scenes from linguistic descriptions and produce their realistic renderings is arguably one of the most challenging problems in computer vision.
6D Camera Relocalization in Visually Ambiguous Extreme Environments
To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy.
Topological Deep Learning: Going Beyond Graph Data
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations.
Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization
We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA).
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders.
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks
Our experiments demonstrate that constraining the synchronization on the Riemannian manifold $SO(n)$ improves the estimation of the functional maps, while our RLFM sampler provides for the first time an uncertainty quantification of the results.
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters.
Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization
We present SyNoRiM, a novel way to jointly register multiple non-rigid shapes by synchronizing the maps relating learned functions defined on the point clouds.
Projective Manifold Gradient Layer for Deep Rotation Regression
Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem.
HuMoR: 3D Human Motion Model for Robust Pose Estimation
We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape.
Weakly Supervised Learning of Rigid 3D Scene Flow
We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies.
Quantum Permutation Synchronization
We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision.
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds.
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects.
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
Deformation-Aware 3D Model Embedding and Retrieval
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.
Synchronizing Probability Measures on Rotations via Optimal Transport
We introduce a new paradigm, $\textit{measure synchronization}$, for synchronizing graphs with measure-valued edges.
Continuous Geodesic Convolutions for Learning on 3D Shapes
In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.
From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds
We propose a new method for segmentation-free joint estimation of orthogonal planes, their intersection lines, relationship graph and corners lying at the intersection of three orthogonal planes.
Learning multiview 3D point cloud registration
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Quaternion Equivariant Capsule Networks for 3D Point Clouds
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points.
Probabilistic Permutation Synchronization using the Riemannian Structure of the Birkhoff Polytope
We present an entirely new geometric and probabilistic approach to synchronization of correspondences across multiple sets of objects or images.
3D Local Features for Direct Pairwise Registration
Our extensive quantitative and qualitative experiments suggests that our approach outperforms the state of the art in challenging real datasets of pairwise registration and that augmenting the keypoints with local pose information leads to better generalization and a dramatic speed-up.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points.
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Ranked #5 on
3D Object Classification
on ModelNet40
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.
Survey of Higher Order Rigid Body Motion Interpolation Methods for Keyframe Animation and Continuous-Time Trajectory Estimation
In this survey we carefully analyze the characteristics of higher order rigid body motion interpolation methods to obtain a continuous trajectory from a discrete set of poses.
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry.
Ranked #12 on
Point Cloud Registration
on 3DMatch Benchmark
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm for initializing pose graph optimization problems, arising in various scenarios such as SFM (structure from motion) or SLAM (simultaneous localization and mapping).
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure.
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds.
Ranked #14 on
Point Cloud Registration
on 3DMatch Benchmark
CAD Priors for Accurate and Flexible Instance Reconstruction
With aid of this prior acting as a proxy, we propose a fully enhanced pipeline, capable of automatically detecting and segmenting the object of interest from scenes and creating a pose graph, online, with linear complexity.
Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions
Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems.
A Novel Method for Vectorization
Vectorization of images is a key concern uniting computer graphics and computer vision communities.
