Firstname Lastname

Linjie Lyu

Max-Planck-Institut für Informatik
D6: Visual Computing and Artificial Intelligence
 office: Campus E1 4, Room 117
Saarland Informatics Campus
66123 Saarbrücken
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 phone: +49 681 9325 4548
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Research Interests

  • Computer Vision, Computer Graphics, Machine Learning
  • Differentiable rendering


Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination

Linjie Lyu   Ayush Tewari   Thomas Leimkühler    Marc Habermann   Christian Theobalt  

(ECCV 2022 Oral)

Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics. On the one hand, most existing works in Computer Vision usually impose many assumptions regarding the image formation process, e.g. direct illumination and predefined materials, to make scene parameter estimation tractable. On the other hand, mature Computer Graphics tools allow modeling of complex photo-realistic light transport given all the scene parameters. Combining these approaches, we propose a method for scene relighting under novel views by learning a neural precomputed radiance transfer function, which implicitly handles global illumination effects using novel environment maps. Our method can be solely supervised on a set of real images of the scene under a single unknown lighting condition. To disambiguate the task during training, we tightly integrate a differentiable path tracer in the training process and propose a combination of a synthesized OLAT and a real image loss. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the art and, thus, also our re-rendering results are more realistic and accurate.

[pdf], [video], [project page], [arxiv], [code]

Efficient and Differentiable Shadow Computation for Inverse Problems

Linjie Lyu   Marc Habermann   Lingjie Liu   Mallikarjun B R   Ayush Tewari  
Christian Theobalt  

ICCV 2021

Differentiable rendering has received increasing interest for image-based inverse problems. It can benefit traditional optimization-based solutions to inverse problems, but also allows for self-supervision of learning-based approaches for which training data with ground truth annotation is hard to obtain. However, existing differentiable renderers either do not model visibility of the light sources from the different points in the scene, responsible for shadows in the images, or are too slow for being used to train deep architectures over thousands of iterations. To this end, we propose an accurate yet efficient approach for differentiable visibility and soft shadow computation. Our approach is based on the spherical harmonics approximations of the scene illumination and visibility, where the occluding surface is approximated with spheres. This allows for a significantly more efficient shadow computation compared to methods based on ray tracing. As our formulation is differentiable, it can be used to solve inverse problems such as texture, illumination, rigid pose, and geometric deformation recovery from images using analysis-by-synthesis optimization.

[pdf], [video], [project page], [arxiv]


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