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).
#4 best model for 3D Object Reconstruction on Data3D−R2N2
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.
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.
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.
We consider the problem of scaling deep generative shape models to high-resolution.
Mesh models are a promising approach for encoding the structure of 3D objects.
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge.
The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic single-view visual hull inside of the network.
Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.
#3 best model for 3D Object Reconstruction on Data3D−R2N2