4 citations · 7 across the 12 of their papers we have counts for
6 papers · 1 filter
LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection
Johannes Meier, Jonathan Michel, Oussema Dhaouadi +7
Real-time monocular 3D object detection remains challenging due to severe depth ambiguity, viewpoint shifts, and the high computational cost of 3D reasoning. Existing approaches ei…
A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation
Damjan Kalšan, Denis Zavadski, Tim Küchler +3
Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real imagery. This gap is particularly…
Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery
Xinrui Gong, Oliver Hahn, Christoph Reich +4
Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage o…
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Aleksandar Jevtić, Christoph Reich, Felix Wimbauer +4
Semantic scene completion (SSC) aims to infer both the 3D geometry and semantics of a scene from single images. In contrast to prior work on SSC that heavily relies on expensive gr…
Scene-Centric Unsupervised Panoptic Segmentation
Oliver Hahn, Christoph Reich, Nikita Araslanov +3
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In con…
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Jannik Endres, Oliver Hahn, Charles Corbière +3
Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-e…