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Found 6 papers, 1 papers with code
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks.
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space.
A Simple and Scalable Shape Representation for 3D Reconstruction
Deep learning applied to the reconstruction of 3D shapes has seen growing interest.
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization.
Implicit Surface Representations As Layers in Neural Networks
Implicit shape representations, such as Level Sets, provide a very elegant formulation for performing computations involving curves and surfaces.
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
