From the 1 of 7 linked papers with an AI index.
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Do Image Editing Models Understand Lighting?
Tim Küchler, Johann-Friedrich Feiden, Matthias NieÃner +1
The paper introduces a new real‑world HDR benchmark (3DLP) that measures how well generative image‑editing models can edit lighting by turning light probes on and off, and evaluate…
Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging
Max Kahl, Sebastian Stricker, Lisa Hutschenreiter +3
Multi-graph matching is a fundamental problem in computer vision. Our work is motivated by a challenging application in bioimaging, where dozens or even hundreds of 3D microscopy i…
A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation
Denis Zavadski, Damjan Kalšan, Tim Küchler +3
Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real imagery. This gap is particularly…
Online Video Depth Anything: Temporally-Consistent Depth Prediction with Low Memory Consumption
Johann-Friedrich Feiden, Tim Küchler, Denis Zavadski +2
Depth estimation from monocular video has become a key component of many real-world computer vision systems. Recently, Video Depth Anything (VDA) has demonstrated strong performanc…
Product-Quantised Image Representation for High-Quality Image Synthesis
Denis Zavadski, Nikita Philip Tatsch, Carsten Rother
Product quantisation (PQ) is a classical method for scalable vector encoding, yet it has seen limited usage for latent representations in high-fidelity image generation. In this wo…
PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage
Denis Zavadski, Damjan Kalšan, Carsten Rother
This work addresses the task of zero-shot monocular depth estimation. A recent advance in this field has been the idea of utilising Text-to-Image foundation models, such as Stable…