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.
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).
Ranked #4 on 3D Reconstruction on Data3D−R2N2
Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.
Ranked #2 on 3D Reconstruction on Data3D−R2N2 (using extra training data)
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.
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.
Ranked #1 on 3D Object Reconstruction on Data3D−R2N2
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.
We consider the problem of scaling deep generative shape models to high-resolution.
Ranked #2 on 3D Object Reconstruction on Data3D−R2N2 (Avg F1 metric)
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.
Mesh models are a promising approach for encoding the structure of 3D objects.
Ranked #1 on 3D Object Reconstruction on Data3D−R2N2 (Avg F1 metric)