DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD
Models for 2.5D Recognition

DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition

27 February 2017

Benjamin Planche

Ziyan Wu

Stefan Kluckner

Sergey Zakharov

Papers citing "DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition"

5 / 5 papers shown

Title
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods Laurent Valentin Jospin A. Antony Lian Xu Hamid Laga F. Boussaïd Bennamoun 24 4 0 17 Sep 2022
Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets Ji-Woo Kim Minchang Kim Y. Shin Minyoung Chung 3DPC 24 1 0 14 Jul 2022
A Survey on RGB-D Datasets Alexandre Lopes Roberto Souza Hélio Pedrini 3DV MDE 26 33 0 15 Jan 2022
Synthetic Data for Deep Learning Sergey I. Nikolenko 46 348 0 25 Sep 2019
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping Yunzhi Lin Chao Tang Fu-Jen Chu Patricio A. Vela 3DPC 13 39 0 12 Sep 2019