Philipp Slusallek

Image processing workflows typically consist of a series of different functions, each working with “image” inputs and outputs in an abstract sense. However, the specific in-memory representation of images differs between and sometimes within libraries. Conversion is therefore necessary when integrating functions from several sources into a single program. The conversion process forces users to consider low-level implementation details, including data types, color channels, channel order, minibatch layout, memory locations, and pixel intensity ranges. Specifically in the case of visual programming languages (VPLs), this distracts from high-level operations. We introduce im2im, a Python library that automates the conversion of in-memory image representations. The central concept of this library is a knowledge graph that describes image representations and how to convert between them. The system queries this knowledge graph to generate the conversion code and execute it, converting an image to the desired representation. The effectiveness of the approach is evaluated through two case studies in VPLs. In each case, we compared a workflow created in a basic block-based VPL with the same workflow enhanced using im2im. These evaluations show that im2im automates type conversions and eliminates the need for manual intervention. Additionally, we compared the overhead of using explicit intermediate representations versus im2im, both of which avoid manual type conversions in VPLs. The results indicate that im2im generates only the necessary conversions, avoiding the runtime overhead associated with converting to and from intermediate formats. A performance comparison between the step-by-step approach used by im2im and a single-function approach demonstrates that the overhead introduced by im2im does not impact practical usability. While focused on block-based VPLs, im2im can be generalized to other VPLs and textual programming environments. Its principles are also applicable to domains other than images. The source code and analyses are available via GitHub.

Inverse rendering has emerged as a standard tool to reconstruct the parameters of appearance models from images (e.g., textured BSDFs). In this work, we present several novel contributions motivated by the practical challenges of recovering high-resolution surface appearance textures, including spatially-varying subsurface scattering parameters. First, we propose Laplacian mipmapping , which combines differentiable mipmapping and a Laplacian pyramid representation into an effective preconditioner. This seemingly simple technique significantly improves the quality of recovered surface textures on a set of challenging inverse rendering problems. Our method automatically adapts to the render and texture resolutions, only incurs moderate computational cost and achieves better quality than prior work while using fewer hyperparameters. Second, we introduce a specialized gradient computation algorithm for textured, path-traced subsurface scattering, which facilitates faithful reconstruction of translucent materials. By using path tracing, we enable the recovery of complex appearance while avoiding the approximations of the previously used diffusion dipole methods. Third, we demonstrate the application of both these techniques to reconstructing the textured appearance of human faces from sparse captures. Our method recovers high-quality relightable appearance parameters that are compatible with current production renderers.

Multiple importance sampling (MIS) is a vital component of most rendering algorithms. MIS computes a weighted sum of samples from many different techniques to achieve generalization, that is, to handle a wide range of scene types and lighting effects. A key factor to the performance of MIS is the choice of weighting function. The go-to default - the balance heuristic - performs well in many cases, but prior work has shown that it can yield unsatisfactory results. A number of challenges cause this suboptimal performance, including low-variance techniques, sample correlation, and unknown sampling densities. Prior work has suggested improvements for some of these problems, but a general optimal solution has yet to be found. We propose a general and practical weight correction scheme: We optimize, on-the-fly, a set of correction factors that are multiplied into any baseline MIS heuristic (e.g., balance or power). We demonstrate that this approach yields consistently better equal-time performance on two rendering applications: bidirectional algorithms and resampled importance sampling for direct illumination.

Instrumented insoles have created opportunities for patient monitoring via long-term recordings of ground reaction forces (GRFs). As the GRF curve is altered in patients after lower-extremity fracture, parameters defined on established curve landmarks often cannot be used to monitor the early rehabilitation process. We aimed to screen several new GRF curve-based parameters for suitability and hypothesized an interrelation with days after surgery. In an observational longitudinal study, data were collected from 13 patients with tibial fractures during straight walking at hospital visits using instrumented insoles. Parametrized curves were fitted and regression analyses conducted to determine the best fit, reflected in the highest R²-value and lowest fitting error. A Wald Test with t-distribution was employed for statistical analysis. Strides were classified as regular or non-regular, and changes in this proportion were analyzed. Among the 12 parameters analyzed, those with the highest R²-values were the mean force between inflection points (R² = 0.715, p < 0.001, t₄₂ = 9.89), the absolute time between inflection points (R² = 0.707, p < 0.001, t₄₂ = 9.83), and the highest overall force (R² = 0.722, p < 0.001, t₄₂ = 10.05). There was a significant increase in regular strides on both injured (R² = 0.427, p < 0.001, t₄₂ = 5.83) and healthy (R² = 0.506, p < 0.001, t₄₂ = 6.89) sides. The proposed parameters and assessment of the regular stride ratio enable new options for analyses and monitoring during rehabilitation after tibial shaft fractures. They are robust to pathologic GRF curves, can be determined independently from spatiotemporal coherence, and thus might provide advantages over established methods.

The deployment and acceptance of Vehicle-to-Everything (V2X) technologies are essential for the advancement of intelligent transportation systems, requiring robust simulation tools for development and validation. This paper offers an overview of leading V2X simulation tools, emphasizing their role in communication and cybersecurity applications. Existing surveys are reviewed, identifying gaps such as the limited coverage of less known tools such as CARLA and CarSim and the absence of detailed market analyses. A thorough assessment of the market significance of V2X simulation tools is provided, highlighting annual trends and profiling leading companies. Publication trends across major scientific databases are also examined, reflecting the growing interest and research diversity in V2X simulations. The review includes an analysis of crucial simulation tools such as SUMO, NS3, OMNeT++, Veins, and CARLA, emphasizing their contributions to V2X communications. Regarding cybersecurity, the study explores simulation parameters for mitigating security threats such as jamming, spoofing, Denial of Service (DoS), and eavesdropping. Furthermore, the increasing integration of different simulation tools is highlighted to address their individual limitations. Finally, emerging research opportunities are identified, advocating for the integration of co-simulation frameworks and AI-driven (Artificial Intelligence) approaches to improve the accuracy and resilience of V2X systems. This survey aims to guide future research efforts in developing secure, efficient, and scalable V2X communication technologies.