7 citations · 12 across the 5 of their papers we have counts for
5 papers
FullFormer: Generating Shapes Inside Shapes
Tejaswini Medi, Jawad Tayyub, Muhammad Sarmad +2
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. This work builds…
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations
Jawad Tayyub, Muhammad Sarmad, Nicolas Schönborn
Explaining decisions made by deep neural networks is a rapidly advancing research topic. In recent years, several approaches have attempted to provide visual explanations of decisi…
LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representation
Abol Basher, Muhammad Sarmad, Jani Boutellier
Recently, several works have addressed modeling of 3D shapes using deep neural networks to learn implicit surface representations. Up to now, the majority of works have concentrate…
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion
Muhammad Sarmad, Hyunjoo Jenny Lee, Young Min Kim
We present RL-GAN-Net, where a reinforcement learning (RL) agent provides fast and robust control of a generative adversarial network (GAN). Our framework is applied to point cloud…
Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network
Juan Luis Gonzalez, Muhammad Sarmad, Hyunjoo J. Lee +1
Deep convolutional neural networks (DCNN) have recently shown promising results in low-level computer vision problems such as optical flow and disparity estimation, but still, have…