Philipp Schröppel
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View article: Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users Open
The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationships in different ways. This observation holds even for intri…
View article: Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond Open
In recent years, research on out-of-distribution (OoD) detection for semantic segmentation has mainly focused on road scenes -- a domain with a constrained amount of semantic diversity. In this work, we challenge this constraint and extend…
View article: Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation
Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation Open
Controllable generation of 3D assets is important for many practical applications like content creation in movies, games and engineering, as well as in AR/VR. Recently, diffusion models have shown remarkable results in generation quality o…
View article: SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection Open
We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is…
View article: A Benchmark and a Baseline for Robust Multi-view Depth Estimation
A Benchmark and a Baseline for Robust Multi-view Depth Estimation Open
Recent deep learning approaches for multi-view depth estimation are employed either in a depth-from-video or a multi-view stereo setting. Despite different settings, these approaches are technically similar: they correlate multiple source …
View article: Semi-Supervised Disparity Estimation with Deep Feature Reconstruction
Semi-Supervised Disparity Estimation with Deep Feature Reconstruction Open
Despite the success of deep learning in disparity estimation, the domain generalization gap remains an issue. We propose a semi-supervised pipeline that successfully adapts DispNet to a real-world domain by joint supervised training on lab…