17 citations · 18 across the 4 of their papers we have counts for
11 papers · 1 filter
SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection
Mathis Kruse, Marco Rudolph, Dominik Woiwode +1
Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult set…
Personalized 3D Human Pose and Shape Refinement
Tom Wehrbein, Bodo Rosenhahn, Iain Matthews +1
Recently, regression-based methods have dominated the field of 3D human pose and shape estimation. Despite their promising results, a common issue is the misalignment between predi…
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation
Mariia Khan, Yue Qiu, Yuren Cong +3
Multi-class multi-instance segmentation is the task of identifying masks for multiple object classes and multiple instances of the same class within an image. The foundational Segm…
Robust Shape Fitting for 3D Scene Abstraction
Florian Kluger, Eric Brachmann, Michael Ying Yang +1
Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such a…
PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus
Florian Kluger, Bodo Rosenhahn
We present a real-time method for robust estimation of multiple instances of geometric models from noisy data. Geometric models such as vanishing points, planar homographies or fun…
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Patrick Glandorf, Timo Kaiser, Bodo Rosenhahn
Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularize…