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We show how shadow mapping, a method for approximating visibility in real-time graphics, can be made differentiable. Our central observation is that pre-filtered shadow mapping, in combination with differentiable rasterizers, permits the efficient generation of shadow derivatives. In gradient-based optimizations, differentiable rasterizers with shadow mapping can be an efficient alternative to differentiable ray tracing. The paper has been accepted to CVPR 2023. Additional information, including a presentation video and the source code, can be found on the project page.

Prof. Alexa has been awarded an ERC Advanced Grant for his research project “EMERGE” (“Geometry Processing as Inference”). The European Research Council will finance the project with 2.5 million euros over the next five years. They exclusively fund groundbreaking, innovative and pioneering basic research. The applicants’ scientific excellence and the projects are the sole selection criterion. 

EMERGE aims to use the methods of geometry processing also for the processing of higher-dimensional structures. The hope and central thesis of the project is that the refinement of methods in geometry processing over the last decades will still be successful when extended to higher dimensions and exploit potentials that are complementary to developments in machine intelligence and classical digital signal processing.

CG group members submitted two papers to CVPR 2022 and both were accepted. Markus Worchel’s work Multi-View Mesh Reconstruction with Deferred Shading is on recovering geometry from calibrated input images based on neural shading. Marc Alexa’s Super-Fibonacci Spirals provide a simple method for quickly generating orientation samples with low discrepancy.

We developed a normal driven stylization tool. The set of preferred normals can be chosen arbitrarily from the Gauss sphere, including semi-discrete sets to model preference for cylinder- or cone-like shapes. The resulting paper has been accepted for presentation at this year’s Symposium on Geometry Processing. We created a project web site. where you can find more information like the presentation video and code.

In joint work with ETH Zurich we have investigated the properties of different Laplace operators for tetrahedral meshes. The resulting paper has been accepted for presentation at this year’s Symposium on Geometry Processing and received the best paper award. More information on the work, including presentation slides and code, can be found at the project web site (hosted at ETH Zurich).

We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.

See our project page for more details.

Humans involuntarily move their eyes when retrieving an image from memory. This motion is often similar to actually observing the image. We suggest to exploit this behavior as a new modality in human computer interaction, using the motion of the eyes as a descriptor of the image. Interaction requires the user’s eyes to be tracked but no voluntary physical activity. We perform a controlled experiment and develop matching techniques using machine learning to investigate if images can be discriminated based on the gaze patterns recorded while users merely think about image. Our results indicate that image retrieval is possible with an accuracy significantly above chance. We also show that this result generalizes to images not used during training of the classifier and extends to uncontrolled settings in a realistic scenario.

Check out our project page for more details.

We provide the first large dataset of human fixations on physical 3D objects presented in varying viewing conditions and made of different materials. Our experimental setup is carefully designed to allow for accurate calibration and measurement. We estimate a mapping from the pair of pupil positions to 3D coordinates in space and register the presented shape with the eye tracking setup. By modeling the fixated positions on 3D shapes as a probability distribution, we analysis the similarities among different conditions. The resulting data indicates that salient features depend on the viewing direction. Stable features across different viewing directions seem to be connected to semantically meaningful parts. We also show that it is possible to estimate the gaze density maps from view dependent data. The dataset provides the necessary ground truth data for computational models of human perception in 3D.

Check out our project page for more details.

Prof. Alexa has been selected as Editor in Chief of ACM Transactions on Graphics (TOG), the leading technical journal in the field of computer graphics.