Search Results for author:
Found 9 papers, 4 papers with code
Blind Localization and Clustering of Anomalies in Textures
By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation.
Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Sensors
In contrast, we propose a novel, joint calibration approach for optical RGB-D sensors and MIMO radars that is designed to operate in the radar's near-field range, within decimeters from the sensors.
Achieving Efficient and Realistic Full-Radar Simulations and Automatic Data Annotation by exploiting Ray Meta Data of a Radar Ray Tracing Simulator
This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object.
High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis
We propose a novel method for Zero-Shot Anomaly Localization on textures.
PhotoApp: Photorealistic Appearance Editing of Head Portraits
We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination in a portrait image.
Neural BRDF Representation and Importance Sampling
Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known.
Monocular Reconstruction of Neural Face Reflectance Fields
The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing.
Efficient Spatially Adaptive Convolution and Correlation
Fast methods for convolution and correlation underlie a variety of applications in computer vision and graphics, including efficient filtering, analysis, and simulation.
Learning on the Edge: Explicit Boundary Handling in CNNs
Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding.
