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"

8 / 8 papers shown

Title
LRM-Zero: Training Large Reconstruction Models with Synthesized Data Desai Xie Sai Bi Zhixin Shu Kai Zhang Zexiang Xu Yi Zhou Soren Pirk Arie E. Kaufman Xin Sun Hao Tan SyDa 61 14 0 13 Jun 2024
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 32 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 29 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 46 349 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 21 39 0 12 Sep 2019
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only Sergey Zakharov Benjamin Planche Ziyan Wu Andreas Hutter H. Kosch Slobodan Ilic 27 23 0 24 Apr 2018
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching G. Georgakis Srikrishna Karanam Ziyan Wu Jan Ernst Jana Kosecka 3DPC 3DV 20 62 0 22 Feb 2018