collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

Temporal Preservation over Processing: Diagnosing and Designing Spatiotemporal Single-Stage Video Detectors

Karam Tomotaki-Dawoud, Anna Hilsmann, Peter Eisert +1

Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or m…

cs.CV2026

Non-invasive Growth Monitoring of Small Freshwater Fish in Home Aquariums via Stereo Vision

Clemens Seibold, Anna Hilsmann, Peter Eisert

Monitoring fish growth behavior provides relevant information about fish health in aquaculture and home aquariums. Yet, monitoring fish sizes poses different challenges, as fish ar…

cs.CV2026

Video-based Locomotion Analysis for Fish Health Monitoring

Timon Palm, Clemens Seibold, Anna Hilsmann +1

Monitoring the health conditions of fish is essential, as it enables the early detection of disease, safeguards animal welfare, and contributes to sustainable aquaculture practices…

cs.CV2026

R3GW: Relightable 3D Gaussians for Outdoor Scenes in the Wild

Margherita Lea Corona, Wieland Morgenstern, Peter Eisert +1

3D Gaussian Splatting (3DGS) has established itself as a leading technique for 3D reconstruction and novel view synthesis of static scenes, achieving outstanding rendering quality…

cs.CV2026

Phys-3D: Physics-Constrained Real-Time Crowd Tracking and Counting on Railway Platforms

Bin Zeng, Johannes Künzel, Anna Hilsmann +1

Accurate, real-time crowd counting on railway platforms is essential for safety and capacity management. We propose to use a single camera mounted in a train, scanning the platform…

cs.CV2025

AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards

Laura-Sophia von Hirschhausen, Jannes S. Magnusson, Mykyta Kovalenko +6

Deep learning has transformed computer vision for precision agriculture, yet apple orchard monitoring remains limited by dataset constraints. The lack of diverse, realistic dataset…