4 citations · 8 across the 17 of their papers we have counts for
7 papers · 1 filter
Counting Contributions, Not Views: Occlusion-Aware Densification for 3D Gaussian Splatting
Marcel Rogge, René Schuster, Didier Stricker
3D Gaussian Splatting grows a dense scene from a sparse initialization through adaptive density control (ADC), driven by per-Gaussian statistics gathered during rendering. These st…
Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Cameras
Katharina Bendig, René Schuster, Didier Stricker
Event cameras follow a retina-inspired sensing principle, reporting local intensity changes asynchronously with hightemporal resolution and a wide dynamic range. Spiking Neural Net…
MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
Shishir Muralidhara, Didier Stricker, René Schuster
Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Expert-based learning, in which t…
Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training
Aheli Saha, René Schuster, Didier Stricker
Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities. These features provide a high dynamic range and sign…
LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
Aditya Ranjan Dash, Ramy Battrawy, René Schuster +1
Advanced autonomous systems rely on multi-sensor fusion for safer and more robust perception. To enable effective fusion, calibrating directly from natural driving scenes (i.e., ta…
SF3D-RGB: Scene Flow Estimation from Monocular Camera and Sparse LiDAR
Rajai Alhimdiat, Ramy Battrawy, René Schuster +2
Scene flow estimation is an extremely important task in computer vision to support the perception of dynamic changes in the scene. For robust scene flow, learning-based approaches…
SAILS: Segment Anything with Incrementally Learned Semantics for Task-Invariant and Training-Free Continual Learning
Shishir Muralidhara, Didier Stricker, René Schuster
Continual learning remains constrained by the need for repeated retraining, high computational costs, and the persistent challenge of forgetting. These factors significantly limit…