1 citations · 1 across the 9 of their papers we have counts for
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CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation
Samer Abualhanud, Max Mehltretter
Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving. Multi-view surround camera rigs provide broad scene coverage, but the spatial…
Semantic Segmentation of Textured Non-manifold 3D Meshes using Transformers
Mohammadreza Heidarianbaei, Max Mehltretter, Franz Rottensteiner
Textured 3D meshes jointly represent geometry, topology, and appearance, yet their irregular structure poses significant challenges for deep-learning-based semantic segmentation. W…
Open-Vocabulary Semantic Segmentation in Remote Sensing via Hierarchical Attention Masking and Model Composition
Mohammadreza Heidarianbaei, Mareike Dorozynski, Hubert Kanyamahanga +2
In this paper, we propose ReSeg-CLIP, a new training-free Open-Vocabulary Semantic Segmentation method for remote sensing data. To compensate for the problems of vision language mo…
CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimation
Samer Abualhanud, Christian Grannemann, Max Mehltretter
Self-supervised surround-view depth estimation enables dense, low-cost 3D perception with a 360° field of view from multiple minimally overlapping images. Yet, most existing method…
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Markus Hillemann, Robert Langendörfer, Max Heiken +6
Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene r…
Depth-aware Panoptic Segmentation
Tuan Nguyen, Max Mehltretter, Franz Rottensteiner
Panoptic segmentation unifies semantic and instance segmentation and thus delivers a semantic class label and, for so-called thing classes, also an instance label per pixel. The di…
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
Max Meyer, Amadeus Langer, Max Mehltretter +5
Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO emissions that are associated with the produc…
Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning
Max Mehltretter
Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in rece…
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
Max Mehltretter, Christian Heipke
Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous dr…