Ovsjanikov, M.[Maks]
Co Author Listing * Affection: Learning Affective Explanations for Real-World Visual Data
* Affine invariant visual phrases for object instance recognition
* ArtEmis: Affective Language for Visual Art
* Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features
* Correspondence-Free Region Localization for Partial Shape Similarity via Hamiltonian Spectrum Alignment
* Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
* Deep orientation-aware functional maps: Tackling symmetry issues in Shape Matching
* Detection of Mirror-Symmetric Image Patches
* DPFM: Deep Partial Functional Maps
* DWKS: A Local Descriptor of Deformations Between Meshes and Point Clouds
* Effective Rotation-Invariant Point CNN with Spherical Harmonics Kernels
* Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces
* Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps
* GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space
* Generalizable Local Feature Pre-training for Deformable Shape Analysis
* Image webs: Computing and exploiting connectivity in image collections
* Implicit Field Supervision for Robust Non-rigid Shape Matching
* Instant recovery of shape from spectrum via latent space connections
* Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation
* Isospectralization, or How to Hear Shape, Style, and Correspondence
* Learning Delaunay Surface Elements for Mesh Reconstruction
* Memory-Scalable and Simplified Functional Map Learning
* OperatorNet: Recovering 3D Shapes From Difference Operators
* Persistence-Based Pooling for Shape Pose Recognition
* Persistence-based segmentation of deformable shapes
* Persistence-Based Structural Recognition
* Physical Simulation Layer for Accurate 3D Modeling
* Physically-aware Generative Network for 3D Shape Modeling
* Pointtrinet: Learned Triangulation of 3d Point Sets
* PoNQ: A Neural QEM-Based Mesh Representation
* Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering
* ReVISOR: ResUNets with visibility and intensity for structured outlier removal
* RIVQ-VAE: Discrete Rotation-Invariant 3D Representation Learning
* SATR: Zero-Shot Semantic Segmentation of 3D Shapes
* Self-Supervised Dual Contouring
* Shape Google: a computer vision approach to isometry invariant shape retrieval
* SHREC'10 Track: Correspondence Finding
* SHREC'10 Track: Feature Detection And Description
* SHREC'10 Track: Robust Shape Retrieval
* Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
* Spatially and Spectrally Consistent Deep Functional Maps
* Spectral Shape Recovery and Analysis Via Data-driven Connections
* SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
* Supervised Descriptor Learning for Non-Rigid Shape Matching
* To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning
* Understanding and Improving Features Learned in Deep Functional Maps
* Unsupervised Deep Learning for Structured Shape Matching
* Unsupervised Multi-class Joint Image Segmentation
* Unsupervised Representation Learning for Diverse Deformable Shape Collections
* VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
Includes: Ovsjanikov, M.[Maks] Ovsjanikov, M.
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