3D Object Reconstruction
61 papers with code • 4 benchmarks • 7 datasets
Image: Choy et al
Benchmarks
These leaderboards are used to track progress in 3D Object Reconstruction
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Libraries
Use these libraries to find 3D Object Reconstruction models and implementationsDatasets
ShapeNet
BEHAVE
X3D
Parcel3D
A collection of 131 CT datasets of pieces of modeling clay containing stones
Corn Seeds Dataset
Latest papers with no code
SimNP: Learning Self-Similarity Priors Between Neural Points
(1) We design the first neural point representation on a category level by utilizing the concept of coherent point clouds.
Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior
We propose a method to model uncertainty as part of the representation and define an uncertainty-aware encoder which generates latent codes with uncertainty directly from individual input images.
DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model
Our DITTO-NeRF consists of constructing high-quality partial 3D object for limited in-boundary (IB) angles using the given or text-generated 2D image from the frontal view and then iteratively reconstructing the remaining 3D NeRF using inpainting latent diffusion model.
Visibility Aware Human-Object Interaction Tracking from Single RGB Camera
In this work, we propose a novel method to track the 3D human, object, contacts between them, and their relative translation across frames from a single RGB camera, while being robust to heavy occlusions.
SnakeVoxFormer: Transformer-based Single Image\\Voxel Reconstruction with Run Length Encoding
The key novelty of our approach is in using the run-length encoding that traverses (like a snake) the voxel space and encodes wide spatial differences into a 1D structure that is suitable for transformer encoding.
Efficient 3D Object Reconstruction using Visual Transformers
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
Learning Neural Implicit Surfaces with Object-Aware Radiance Fields
Then, we build the geometric correspondence between 2D planes and 3D meshes by rasterization, and project the estimated object regions into 3D explicit object surfaces by aggregating the object information across multiple views.
Multi-View Neural Surface Reconstruction with Structured Light
Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision.
3D Reconstruction of Multiple Objects by mmWave Radar on UAV
The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space.
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning
This method cuts a part of a 3D shape in each step which is then approximated as a polygon mesh.
