• The core idea is to detect illumination in a pre-defined scene (Digital Twin) and adapt the moving objects in the simulation.

  In this work, the student would have to:

  • Create a sensor setup
  • Inverse Rendering
  • Show lighting changes in the room
  • Estimate novel views

Preferable

• Experience with Git
• Python (Pytorch)
• Nvidia Omniverse

Using monocular RGB data to reconstruct a 3D interior environment with CAD-based reconstruction.

Git, Python, PyTorch

The task is to generate an End-to-End pipeline for illumination estimation inside of a digital twin.

Finally, also an AR application can be created.

Possible References

[1] https://arxiv.org/pdf/1905.02722.pdf

[2] https://arxiv.org/pdf/1906.07370.pdf

[3] https://arxiv.org/pdf/2011.10687.pdf

• Python (Pytorch)
• Experience with Git

3D object detection has been a major task for point cloud-based 3D reconstruction of indoor environments. Current research has focused on having a low inference time for 3D object detection. While this is preferable, a lot of cases do not profit from this. Especially considering the use of a pre-defined static Digital Twin for AR and robotics application, thus this decreases the incentive for low inference time at the cost of accuracy.

As such this thesis will follow the approach of [1] (in this work only based on point cloud data) to generate proposals of layout and objects in a scene through for example [2]/[3] and use some form of optimization algorithm (reinforcement learning, genetic algorithm) to optimize to the correct solution.

Further, for more geometrical-reasonable results the use of a relationship graph neural network, as in [4], would be applied in the pipeline.

References

[1] Hampali, Shreyas, et al. “Monte Carlo Scene Search for 3D Scene Understanding.” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  (2021): 13799-13808. https://arxiv.org/abs/2103.07969#:~:text=We explore how a general, from noisy RGB-D scans.

[2] Chen, Xiaoxue, Hao Zhao, Guyue Zhou, and Ya-Qin Zhang. “PQ-Transformer: Jointly Parsing 3D Objects and Layouts From Point Clouds.” IEEE Robotics and Automation Letters  7 (2022): 2519-2526. https://arxiv.org/abs/2109.05566

[3] Qi, C., Or Litany, Kaiming He and Leonidas J. Guibas. “Deep Hough Voting for 3D Object Detection in Point Clouds.” 2019 IEEE/CVF International Conference on Computer Vision (ICCV)  (2019): 9276-9285. https://arxiv.org/abs/1904.09664

[4] Avetisyan, Armen, Tatiana Khanova, Christopher Bongsoo Choy, Denver Dash, Angela Dai and Matthias Nießner. “SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans.” ArXiv  abs/2003.12622 (2020): n. pag. https://arxiv.org/abs/2003.12622

• Python (Pytorch)
• Experience with Git
• Knowledge in working with 3D Point Clouds (preferable)
• Knowledge about optimization methods (preferable)