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20242026
most citedScene-Centric Unsupervised Panoptic Segmentation

2 citations · 2 across the 5 of their papers we have counts for

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cs.CV2026

Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos

Mohamed Afham, Christoph Reich, Oliver Hahn +2

Procedure planning seeks to estimate a sequence of actions to transition from an observed initial state to a given goal state. Current procedure planning approaches directly predic…

cs.CV2026

Scene-Centric Unsupervised Video Panoptic Segmentation

Christoph Reich, Oliver Hahn, Nikita Araslanov +4

Video panoptic segmentation (VPS) aims to jointly detect, segment, and track all objects while partitioning the video into semantically consistent regions. We introduce the task se…

cs.CV2025

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…

cs.CV2025

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…

cs.CV20252 cited

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…

cs.CV2025

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…