Applied Vision
Authors: Matvey Ivanov (TU Wien) , Markus Vincze (TU Wien) , Peter Hönig (TU Wien)
Edge detection of complex, smooth, transparent, reflective and texture-less objects is an unsolved problem in computer vision. In this work, an existing approach using diffusion in the image space is adapted to enable fast and accurate edge detection. The method is applied to texture-less industrial objects from the T-LESS and XYZIBD datasets. The models are trained on datasets, generated synthetically using BlenderProc. Three training datasets are created using T-LESS objects to evaluate the impact of edge type and object texturing on prediction quality. Two more datasets are generated using XYZIBD objects to investigate the influence of the crease angle used in edge rendering. The diffusion models are evaluated using the NMSE, SSIM, DICE, and CRISP metrics, to assess accuracy, structural fidelity, and perceptual sharpness. Experiments show that our approach achieves competitive edge prediction quality and consistently outperforms existing diffusion based methods in computational efficiency at a lower resolution, while offering overall better prediction fidelity compared to the Canny edge detector. With a runtime of 95ms per image on an NVIDIA RTX3090, the approach demonstrates suitability for deployment in robotic vision systems. A quantitative edge prediction quality evaluation is conducted on real-world test sets which are extended with the edge ground-truth.
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How to Cite: Ivanov, M. , Vincze, M. & Hönig, P. (2026) “Diffusion Edge Detection Of Texture-less Objects”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).