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Found 5 papers, 1 papers with code
FullFormer: Generating Shapes Inside Shapes
Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes.
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations
In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data.
LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representation
Recently, several works have addressed modeling of 3D shapes using deep neural networks to learn implicit surface representations.
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion
While a GAN is unstable and hard to train, we circumvent the problem by (1) training the GAN on the latent space representation whose dimension is reduced compared to the raw point cloud input and (2) using an RL agent to find the correct input to the GAN to generate the latent space representation of the shape that best fits the current input of incomplete point cloud.
Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network
We show a supervised end-to-end training of our proposed networks for optical flow and disparity estimations, and an unsupervised end-to-end training for monocular depth and pose estimations.
