Object Reconstruction
78 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Object Reconstruction
A Large Dataset of Object Scans
HOWS
Most implemented papers
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
fanhqme/PointSetGeneration
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CVPR 2017
Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.
3D Object Reconstruction from Hand-Object Interactions
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
chenhsuanlin/3D-point-cloud-generation
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Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
EdwardSmith1884/3D-Object-Super-Resolution
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NeurIPS 2018
We consider the problem of scaling deep generative shape models to high-resolution.
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
hzxie/Pix2Vox
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A multi-scale context-aware fusion module is then introduced to adaptively select high-quality reconstructions for different parts from all coarse 3D volumes to obtain a fused 3D volume.
Grasping Field: Learning Implicit Representations for Human Grasps
korrawe/grasping_field_demo
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Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
xcyan/nips16_PTN
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NeurIPS 2016
We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes.
3D Object Reconstruction from a Single Depth View with Adversarial Learning
In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.
Dense 3D Object Reconstruction from a Single Depth View
Yang7879/3D-RecGAN-extended
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Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions.
