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3D Shape Representation

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Papers

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Showing 51–92 of 92 papers

TitleStatusHype
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction—0
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation—0
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues—0
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture—0
Taylor3DNet: Fast 3D Shape Inference With Landmark Points Based Taylor Series—0
Cloud Sphere: A 3D Shape Representation via Progressive Deformation—0
Adaptive Wavelet Transformer Network for 3D Shape Representation Learning—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation—0
Multiresolution Deep Implicit Functions for 3D Shape Representation—0
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction—0
GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion—0
Dual Mesh Convolutional Networks for Human Shape Correspondence—0
DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces—0
Learning Occupancy Function from Point Clouds for Surface Reconstruction—0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization—0
Novel View Synthesis from Single Images via Point Cloud Transformation—0
Learning Attentive and Hierarchical Representations for 3D Shape Recognition—0
3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications—0
Local Implicit Grid Representations for 3D Scenes—0
Local Implicit Grid Representations for 3D ScenesCode0
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation—0
Local Deep Implicit Functions for 3D ShapeCode0
Geometric Disentanglement for Generative Latent Shape Models—0
Learning Embedding of 3D models with Quadric LossCode0
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks—0
Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing—0
Grounding Language Attributes to Objects using Bayesian Eigenobjects—0
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and GenerationCode0
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation—0
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders—0
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference—0
3D Point Capsule NetworksCode0
Learning Implicit Fields for Generative Shape ModelingCode0
MeshNet: Mesh Neural Network for 3D Shape RepresentationCode0
Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences—0
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape RecognitionCode0
Learning Discriminative 3D Shape Representations by View Discerning NetworksCode0
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition—0
A generalized parametric 3D shape representation for articulated pose estimation—0
Directionally Convolutional Networks for 3D Shape Segmentation—0
Towards the Evolution of Vertical-Axis Wind Turbines using SupershapesCode0
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