|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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).
#5 best model for 3D Reconstruction on Data3D−R2N2
We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image.
#3 best model for 3D Object Reconstruction on Data3D−R2N2
Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation.
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.
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 Reconstruction on Data3D−R2N2 (using extra training data)
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e. g., table legs) from different coarse 3D volumes to obtain a fused 3D volume.
SOTA for 3D Reconstruction on Data3D−R2N2
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.