4 citations · 8 across the 17 of their papers we have counts for
19 papers
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…
CLoRA: Parameter-Efficient Continual Learning with Low-Rank Adaptation
Shishir Muralidhara, Didier Stricker, René Schuster
In the past, continual learning (CL) was mostly concerned with the problem of catastrophic forgetting in neural networks, that arises when incrementally learning a sequence of task…
Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions
Shishir Muralidhara, René Schuster, Didier Stricker
Semantic segmentation for autonomous driving is an even more challenging task when faced with adverse driving conditions. Standard models trained on data recorded under ideal condi…
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation
Niharika Hegde, Shishir Muralidhara, René Schuster +1
In autonomous driving, environment perception has significantly advanced with the utilization of deep learning techniques for diverse sensors such as cameras, depth sensors, or inf…
AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy
Katharina Bendig, René Schuster, Nicole Thiemer +2
The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual pr…
ShapeAug++: More Realistic Shape Augmentation for Event Data
Katharina Bendig, René Schuster, Didier Stricker
The novel Dynamic Vision Sensors (DVSs) gained a great amount of attention recently as they are superior compared to RGB cameras in terms of latency, dynamic range and energy consu…
CLEO: Continual Learning of Evolving Ontologies
Shishir Muralidhara, Saqib Bukhari, Georg Schneider +2
Continual learning (CL) addresses the problem of catastrophic forgetting in neural networks, which occurs when a trained model tends to overwrite previously learned information, wh…
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support
Ramy Battrawy, René Schuster, Didier Stricker
Recent weakly-supervised methods for scene flow estimation from LiDAR point clouds are limited to explicit reasoning on object-level. These methods perform multiple iterative optim…
RMS-FlowNet++: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
Ramy Battrawy, René Schuster, Didier Stricker
The proposed RMS-FlowNet++ is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation that can operate on high-density point clouds. For hie…
ShapeAug: Occlusion Augmentation for Event Camera Data
Katharina Bendig, René Schuster, Didier Stricker
Recently, Dynamic Vision Sensors (DVSs) sparked a lot of interest due to their inherent advantages over conventional RGB cameras. These advantages include a low latency, a high dyn…
Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion
Michael Fürst, Rahul Jakkamsetty, René Schuster +1
The state of the art in 3D object detection using sensor fusion heavily relies on calibration quality, which is difficult to maintain in large scale deployment outside a lab enviro…
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation
Shishir Muralidhara, Sravan Kumar Jagadeesh, René Schuster +1
Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity. More precisely, seman…
CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss
Christian Bailer, Kiran Varanasi, Didier Stricker
Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we presen…