activity
20232026
most citedA Perspective on Deep Vision Performance with Standard Image and Video Codecs

4 citations · 6 across the 10 of their papers we have counts for

collaborators

11 papers

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

RayTun3R: Online Camera Adaptation in 3D Foundation Models

Daniil Sinitsyn, Nikita Araslanov, Daniel Cremers

Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, , and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole imagery, but degrade sharply…

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.CV2026

INSID3: Training-Free In-Context Segmentation with DINOv3

Claudia Cuttano, Gabriele Trivigno, Christoph Reich +3

In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i)…

cs.CV2026

RINO: Rotation-Invariant Non-Rigid Correspondences

Maolin Gao, Shao Jie Hu-Chen, Congyue Deng +3

Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcraf…

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

ControlEvents: Controllable Synthesis of Event Camera Datawith Foundational Prior from Image Diffusion Models

Yixuan Hu, Yuxuan Xue, Simon Klenk +2

In recent years, event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. However, obtaining l…

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.CV2025★ 2 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.CV2024★ 4 cited

A Perspective on Deep Vision Performance with Standard Image and Video Codecs

Christoph Reich, Oliver Hahn, Daniel Cremers +2

Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models.…

cs.CV2023

Deep Event Visual Odometry

Simon Klenk, Marvin Motzet, Lukas Koestler +1

Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based m…