Blender pipeline to generate images for deep learning (BlenderProc) - Maximilian Denninger
This is a two parts talk. It is based on the papers "3D Scene Reconstruction from a Single Viewport" presented at ECCV 2020 and the "BlenderProc" paper. The speaker is the main author of both papers. This is the recording of part 2.
Recording of part 1: • 3D Scene Reconstruction from a Single View...
References to everything covered in the talk: / references_from_double_lecture_photorealistic
Lecture abstract:
We present BlenderProc, which is a modular procedural pipeline, helping in generating real looking images for the training of convolutional neural networks. These can be used in a variety of use cases including segmentation, depth, normal and pose estimation and many others. A key feature of our extension of blender is the simple to use modular pipeline, which was designed to be easily extendable. By offering standard modules, which cover a variety of scenarios, we provide a starting point on which new modules can be created.
arxiv: https://arxiv.org/abs/1911.01911
git: https://github.com/DLR-RM/BlenderProc
00:00 About BlenderProc
1:05 Why Simulation
4:35 Rendering
7:03 Blender
7:55 BlenderProc
11:05 Sim2Real gap
18:23 Pipeline
19:08 Modules
20:35 RGBRender
22:46 Sampler
24:18 Writer
25:12 How To Get Started
27:13 Basic Config File
35:20 Entity Manipulation
39:35 Physics
41:20 Camera Sampling
47:35 Generation Speed
51:40 BOP Challenge ECCV 2020
56:28 Open Source
56:58 Joint work
58:28 Questions
Presenter BIO:
Maximilian Denninger is currently pursuing his PhD at the German Aerospace Center (DLR), where he is a full-time researcher. His research goal is to improve the computer vision on mobile robots, where the training data is always scarce. At the DLR he heads the vision part of an exciting project called SMiLE, where the goal is to design and implement robots, which are able to assist people working in elderly homes. This includes a variety of tasks from semantic segmentation to scene reconstruction. As robots need a natural understanding of their environment to fulfill any kind of task. For that he and his colleagues created BlenderProc, which helps in the generation of data for the training of neural networks. He is advised for his PhD by his department head Dr. Rudolph Triebel, which also works for the Technical University of Munich (TUM), where Max also works as a teaching assistant to help teach the course "Maching Learning for Computer Vision".
Linkedin: / maximilian-denninger
Twitter: / denningermax
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