3D Shape Representation
38 papers with code • 0 benchmarks • 4 datasets
Image: MeshNet
Benchmarks
These leaderboards are used to track progress in 3D Shape Representation
Objectron
Dynamic FAUST
KeypointNet
Foot3D
Latest papers with no code
Unsupervised Occupancy Learning from Sparse Point Cloud
Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio.
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding
The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes.
TIFu: Tri-directional Implicit Function for High-Fidelity 3D Character Reconstruction
Recent advances in implicit function-based approaches have shown promising results in 3D human reconstruction from a single RGB image.
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies.
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Due to advancements in infrastructural modulations, architectural design is one of the most peculiar and tedious processes.
Mosaic-SDF for 3D Generative Models
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes.
Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects
In this paper, we propose a novel self-supervised approach to learn neural implicit shape representation for deformable objects, which can represent shapes with a template shape and dense correspondence in 3D.
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification
Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics.
Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane
Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities.
CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes
We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid neural implicit 3D shape representation that is dramatically more compact than the current state of the art.
