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
Most implemented papers
Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes
We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approximated wind tunnel conditions after being physically instantiated by a 3D printer.
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
code-implementation1/Code9
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With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
Learning Embedding of 3D models with Quadric Loss
Sharp features such as edges and corners play an important role in the perception of 3D models.
Fully Convolutional Geometric Features
chrischoy/FCGF
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International Conference on Computer vision 2019
Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking.
Local Deep Implicit Functions for 3D Shape
google/ldif
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CVPR 2020
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
Curriculum DeepSDF
haidongz-usc/Curriculum-DeepSDF
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ECCV 2020
When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions.
Local Implicit Grid Representations for 3D Scenes
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
Learning Local Neighboring Structure for Robust 3D Shape Representation
Mesh is a powerful data structure for 3D shapes.
Discrete Point Flow Networks for Efficient Point Cloud Generation
Regenerator/dpf-nets
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ECCV 2020
Generative models have proven effective at modeling 3D shapes and their statistical variations.
GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
lkeab/gsnet
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ECCV 2020
GSNet utilizes a unique four-way feature extraction and fusion scheme and directly regresses 6DoF poses and shapes in a single forward pass.
