arXiv subjects

Tobias Fischer

Publications and source records attributed to Tobias Fischer.

At least 37 records · Page 2Linked to original sources

arXiv2026

DisPlace: Discriminative Place Projections for Multi-Reference Visual Place Recognition

A key challenge in Visual Place Recognition (VPR) is matching query images against reference maps captured under diverse environmental conditions and viewpoints. While multiple reference traversals improve robustness, existing fusion strategies either aggregate references uniformly or rely on heuristic selection, without distinguishing descriptor variations that preserve stable place identity from those caused by changing conditions or viewpoints. In this paper, we propose DisPlace, a multi-reference VPR framework that fuses multiple reference descriptors into a single compact and discriminative place representation. DisPlace formulates descriptor fusion as a generalized eigenvalue problem that maximizes between-place separability while suppressing within-place variation across references, rather than preserving overall descriptor variance. Unlike existing multi-reference fusion methods, DisPlace exploits variation across reference traversals to identify which linear combinations of descriptor dimensions preserve place identity and which capture condition- or viewpoint-specific variation. We evaluate DisPlace on Oxford RobotCar, Nordland, Pittsburgh30k, and Google Landmarks v2 across six state-of-the-art VPR descriptors. DisPlace outperforms seven multi-reference baselines in 49 out of 54 appearance-varying conditions, consistently improves descriptor-level fusion performance under viewpoint and unstructured settings, and requires less storage during inference than all compared fusion methods.

cs.CV↗

arXiv2026

Charged-current neutrino opacity within the relativistic Hartree-Fock framework for astrophysical simulations of core-collapse supernovae and binary neutron star mergers

Neutrinos and their weak interactions play a vital role in the physics of core-collapse supernovae and binary neutron star mergers. Their description within astrophysical simulations, including the weak rates, is of pivotal importance not only for the prediction of accurate neutrino fluxes and spectra, including the associated conditions relevant to nucleosynthesis, neutrinos are also responsible for heating and cooling of the stellar plasma as well as the transport of lepton number and entropy. In the present article, we develop an essential improvement of the description of the underlying nuclear medium, necessary for the calculations of charged-current weak rates, with the inclusion of explicitly momentum-dependent nuclear interactions. To this end, we introduce the relativistic Hartree-Fock (RHF) approach and the associated momentum-dependent nucleon self-energies. We discuss the resulting neutrino and antineutrino opacities and find large discrepancies comparing the weak rates at the RHF level with those of commonly used relativistic mean-field (RMF) models; in particular, we observe a substantial shift of previously reported large medium-dependent modifications associated with the RMF approach.

astro-ph.HE↗

arXiv2026

ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models

Per-scene optimization methods such as 3D Gaussian Splatting provide state-of-the-art novel view synthesis quality but extrapolate poorly to under-observed areas. Methods that leverage generative priors to correct artifacts in these areas hold promise but currently suffer from two shortcomings. The first is scalability, as existing methods use image diffusion models or bidirectional video models that are limited in the number of views they can generate in a single pass (and thus require a costly iterative distillation process for consistency). The second is quality itself, as generators used in prior work tend to produce outputs that are inconsistent with existing scene content and fail entirely in completely unobserved regions. To solve these, we propose a two-stage pipeline that leverages two key insights. First, we train a powerful bidirectional generative model with a novel opacity mixing strategy that encourages consistency with existing observations while retaining the model's ability to extrapolate novel content in unseen areas. Second, we distill it into a causal auto-regressive model that generates hundreds of frames in a single pass. This model can directly produce novel views or serve as pseudo-supervision to improve the underlying 3D representation in a simple and highly efficient manner. We evaluate our method extensively and demonstrate that it can generate plausible reconstructions in scenarios where existing approaches fail completely. When measured on commonly benchmarked datasets, we outperform all existing baselines by a wide margin, exceeding prior state-of-the-art methods by 1-3 dB PSNR.

cs.CV↗

arXiv2026

Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environments. Existing benchmarks testing performance for underwater domain shift simulate variability through synthetic style transfer. This fails to capture intrinsic scene factors such as visibility, illumination, scene composition, or acquisition factors, limiting analysis of real-world effects. We propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics. Unlike prior benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis. We validate this on public datasets, showing systematic variations across domain factors and revealing hidden failure modes.

cs.CV↗

arXiv2026

AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring

Marine ecosystems face increasing pressure due to climate change, driving the need for scalable, AI-powered monitoring solutions to inform effective conservation and restoration efforts. This paper examines the rapid emergence of underwater AI as a major research frontier and analyzes the factors that have transformed marine perception from a niche application into a catalyst for AI innovation. We identify three convergent drivers: i) environmental necessity for ecosystem-scale monitoring, ii) democratization of underwater datasets through citizen science platforms, and iii) researcher migration from saturated terrestrial computer vision domains. Our analysis reveals how unique underwater challenges - turbidity, cryptic species detection, expert annotation bottlenecks, and cross-ecosystem generalization - are driving fundamental advances in weakly supervised learning, open-set recognition, and robust perception under degraded conditions. We survey emerging trends in datasets, scene understanding and 3D reconstruction, highlighting the paradigm shift from passive observation toward AI-driven, targeted intervention capabilities. The paper demonstrates how underwater constraints are pushing the boundaries of foundation models, self-supervised learning, and perception, with methodological innovations that extend far beyond marine applications to benefit general computer vision, robotics, and environmental monitoring.

cs.RO↗

arXiv2026

Automated Coral Spawn Monitoring for Reef Restoration: The Coral Spawn and Larvae Imaging Camera System (CSLICS)

Coral aquaculture for reef restoration requires accurate and continuous spawn counting for resource distribution and larval health monitoring, but current methods are labor-intensive and represent a critical bottleneck in the coral production pipeline. We propose the Coral Spawn and Larvae Imaging Camera System (CSLICS), which uses low cost modular cameras and object detectors trained using human-in-the-loop labeling approaches for automated spawn counting in larval rearing tanks. This paper details the system engineering, dataset collection, and computer vision techniques to detect, classify and count coral spawn. Experimental results from mass spawning events demonstrate an F1 score of 82.4% for surface spawn detection at different embryogenesis stages, 65.3% F1 score for sub-surface spawn detection, and a saving of 5,720 hours of labor per spawning event compared to manual sampling methods at the same frequency. Comparison of manual counts with CSLICS monitoring during a mass coral spawning event on the Great Barrier Reef demonstrates CSLICS' accurate measurement of fertilization success and sub-surface spawn counts. These findings enhance the coral aquaculture process and enable upscaling of coral reef restoration efforts to address climate change threats facing ecosystems like the Great Barrier Reef.

cs.RO↗

arXiv2026

Event-LAB: Towards Standardized Evaluation of Neuromorphic Localization Methods

Event-based localization research and datasets are a rapidly growing area of interest, with a tenfold increase in the cumulative total number of published papers on this topic over the past 10 years. Whilst the rapid expansion in the field is exciting, it brings with it an associated challenge: a growth in the variety of required code and package dependencies as well as data formats, making comparisons difficult and cumbersome for researchers to implement reliably. To address this challenge, we present Event-LAB: a new and unified framework for running several event-based localization methodologies across multiple datasets. Event-LAB is implemented using the Pixi package and dependency manager, that enables a single command-line installation and invocation for combinations of localization methods and datasets. To demonstrate the capabilities of the framework, we implement two common event-based localization pipelines: Visual Place Recognition (VPR) and Simultaneous Localization and Mapping (SLAM). We demonstrate the ability of the framework to systematically visualize and analyze the results of multiple methods and datasets, revealing key insights such as the association of parameters that control event collection counts and window sizes for frame generation to large variations in performance. The results and analysis demonstrate the importance of fairly comparing methodologies with consistent event image generation parameters. Our Event-LAB framework provides this ability for the research community, by contributing a streamlined workflow for easily setting up multiple conditions.

cs.RO↗

arXiv2026

Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change

Long-term environmental monitoring requires the ability to reconstruct and align 3D models across repeated site visits separated by months or years. However, existing Structure-from-Motion (SfM) pipelines implicitly assume near-simultaneous image capture and limited appearance change, and therefore fail when applied to long-term monitoring scenarios such as coral reef surveys, where substantial visual and structural change is common. In this paper, we show that the primary limitation of current approaches lies in their reliance on post-hoc alignment of independently reconstructed sessions, which is insufficient under large temporal appearance change. We address this limitation by enforcing cross-session correspondences directly within a joint SfM reconstruction. Our approach combines complementary handcrafted and learned visual features to robustly establish correspondences across large temporal gaps, enabling the reconstruction of a single coherent 3D model from imagery captured years apart, where standard independent and joint SfM pipelines break down. We evaluate our method on long-term coral reef datasets exhibiting significant real-world change, and demonstrate consistent joint reconstruction across sessions in cases where existing methods fail to produce coherent reconstructions. To ensure scalability to large datasets, we further restrict expensive learned feature matching to a small set of likely cross-session image pairs identified via visual place recognition, which reduces computational cost and improves alignment robustness.

cs.CV↗

arXiv2026

MapAnything: Universal Feed-Forward Metric 3D Reconstruction

We introduce MapAnything, a unified transformer-based feed-forward model that ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions, and then directly regresses the metric 3D scene geometry and cameras. MapAnything leverages a factored representation of multi-view scene geometry, i.e., a collection of depth maps, local ray maps, camera poses, and a metric scale factor that effectively upgrades local reconstructions into a globally consistent metric frame. Standardizing the supervision and training across diverse datasets, along with flexible input augmentation, enables MapAnything to address a broad range of 3D vision tasks in a single feed-forward pass, including uncalibrated structure-from-motion, calibrated multi-view stereo, monocular depth estimation, camera localization, depth completion, and more. We provide extensive experimental analyses and model ablations demonstrating that MapAnything outperforms or matches specialist feed-forward models while offering more efficient joint training behavior, thus paving the way toward a universal 3D reconstruction backbone.

cs.CV↗

arXiv2026

Ultrasensitive Real-Time Detection of SARS-CoV-2 Proteins with Arrays of Biofunctionalized Graphene Field-Effect Transistors

With the growing interest in graphene field-effect transistors (GFETs) for biosensing applications, there is a strong demand for strategies enabling flexible and multiplexed biofunctionalization, as well as highly parallel, real-time electronic readout integrated with microfluidic control. Here we present a methodology that addresses these challenges by enabling real-time, parallel monitoring of multiple GFETs integrated on a single microfabricated chip within an automated electronic and microfluidic platform. We demonstrate the capabilities of this approach through ultrasensitive detection of the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. GFET chips are functionalized via van der Waals assembly using 1 nm-thick molecular two-dimensional (2D) materials - carbon nanomembranes - which enable multiplexed biofunctionalization. The chips are integrated into a custom-developed microelectronic and microfluidic system that allows parallel, real-time, and automated measurements of 15 GFETs. We present in situ biofunctionalization of the GFETs with antibodies, followed by highly specific detection of the S- and N-proteins with limits of detection down to 10 aM and a dynamic range spanning four orders of magnitude. Owing to its versatility, the presented methodology is readily adaptable for sensing a wide range of biological and chemical targets.

physics.bio-ph↗

arXiv2026

Human-in-the-Loop Segmentation of Multi-species Coral Imagery

Marine surveys by robotic underwater and surface vehicles result in substantial quantities of coral reef imagery, however labeling these images is expensive and time-consuming for domain experts. Point label propagation is a technique that uses existing images labeled with sparse points to create augmented ground truth data, which can be used to train a semantic segmentation model. In this work, we show that recent advances in large foundation models facilitate the creation of augmented ground truth masks using only features extracted by the denoised version of the DINOv2 foundation model and K-Nearest Neighbors (KNN), without any pre-training. For images with extremely sparse labels, we use human-in-the-loop principles to enhance annotation efficiency: if there are 5 point labels per image, our method outperforms the prior state-of-the-art by 19.7% for mIoU. When human-in-the-loop labeling is not available, using the denoised DINOv2 features with a KNN still improves on the prior state-of-the-art by 5.8% for mIoU (5 grid points). On the semantic segmentation task, we outperform the prior state-of-the-art by 13.5% for mIoU when only 5 point labels are used for point label propagation. Additionally, we perform a comprehensive study into the number and placement of point labels, and make several recommendations for improving the efficiency of labeling images with points.

cs.CV↗

arXiv2026

Ensemble-Based Event Camera Place Recognition Under Varying Illumination

Compared to conventional cameras, event cameras provide a high dynamic range and low latency, offering greater robustness to rapid motion and challenging lighting conditions. Although the potential of event cameras for visual place recognition (VPR) has been established, developing robust VPR frameworks under severe illumination changes remains an open research problem. In this paper, we introduce an ensemble-based approach to event camera place recognition that combines sequence-matched results from multiple event-to-frame reconstructions, VPR feature extractors, and temporal resolutions. Unlike previous event-based ensemble methods, which only utilise temporal resolution, our broader fusion strategy delivers significantly improved robustness under varied lighting conditions (e.g., afternoon, sunset, night), achieving a 57% relative improvement in Recall@1 across day-night transitions. We evaluate our approach on two long-term driving datasets (with 8 km per traverse) without metric subsampling, thereby preserving natural variations in speed and stop duration that influence event density. We also conduct a comprehensive analysis of key design choices, including binning strategies, polarity handling, reconstruction methods, and feature extractors, to identify the most critical components for robust performance. Additionally, we propose a modification to the standard sequence matching framework that enhances performance at longer sequence lengths. To facilitate future research, we will release our codebase and benchmarking framework.

cs.CV↗

arXiv2026

Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors

Recent learning-based visual localization methods use global descriptors to disambiguate visually similar places, but existing approaches often derive these descriptors from geometric cues alone (e.g., covisibility graphs), limiting their discriminative power and reducing robustness in the presence of noisy geometric constraints. We propose an aggregator module that learns global descriptors consistent with both geometrical structure and visual similarity, ensuring that images are close in descriptor space only when they are visually similar and spatially connected. This corrects erroneous associations caused by unreliable overlap scores. Using a batch-mining strategy based solely on the overlap scores and a modified contrastive loss, our method trains without manual place labels and generalizes across diverse environments. Experiments on challenging benchmarks show substantial localization gains in large-scale environments while preserving computational and memory efficiency. Code is available at https://github.com/sontung/robust_scr.

cs.CV↗

arXiv2026

ESO Expanding Horizon White Paper: Revealing the properties of matter at supranuclear densities with gravitational waves

Understanding dense matter under extreme conditions is one of the most fundamental puzzles in modern physics. Complex interactions give rise to emergent, collective phenomena. While nuclear experiments and Earth - based colliders provide valuable insights, much of the quantum chromodynamics phase diagram at high density and low temperature remains accessible only through astrophysical observations of neutron stars, neutron star mergers, and stellar collapse. Astronomical observations thus offer a direct window to the physics on subatomic scales with gravitational waves presenting an especially clean channel. Next-generation gravitational - wave observatories, such as the Einstein Telescope, would serve as unparalleled instruments to transform our understanding of neutron star matter. They will enable the detection of up to tens of thousands of binary neutron star and neutron star - black hole mergers per year, a dramatic increase over the few events accessible with current detectors. They will provide an unprecedented precision in probing cold, dense matter during the binary inspiral, exceeding by at least an order of magnitude what current facilities can achieve. Moreover, these observatories will allow us to explore uncharted regimes of dense matter at finite temperatures produced in a subset of neutron star mergers, areas that remain entirely inaccessible to current instruments. Together with multimessenger observations, these measurements will significantly deepen our knowledge of dense nuclear matter.

astro-ph.IM↗

arXiv2025

Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments

Effective monitoring of underwater ecosystems is crucial for tracking environmental changes, guiding conservation efforts, and ensuring long-term ecosystem health. However, automating underwater ecosystem management with robotic platforms remains challenging due to the complexities of underwater imagery, which pose significant difficulties for traditional visual localization methods. We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes. Furthermore, we introduce the SQUIDLE+ VPR Benchmark-the first large-scale underwater VPR benchmark designed to leverage an extensive collection of unstructured data from multiple robotic platforms, spanning time intervals from days to years. The dataset encompasses diverse trajectories, arbitrary overlap and diverse seafloor types captured under varying environmental conditions, including differences in depth, lighting, and turbidity. Our code is available at: https://github.com/bev-gorry/underloc

cs.RO↗

arXiv2025

Going Places: Place Recognition in Artificial and Natural Systems

Place recognition, the ability to identify previously visited locations, is critical for both biological navigation and autonomous systems. This review synthesizes findings from robotic systems, animal studies, and human research to explore how different systems encode and recall place. We examine the computational and representational strategies employed across artificial systems, animals, and humans, highlighting convergent solutions such as topological mapping, cue integration, and memory management. Animal systems reveal evolved mechanisms for multimodal navigation and environmental adaptation, while human studies provide unique insights into semantic place concepts, cultural influences, and introspective capabilities. Artificial systems showcase scalable architectures and data-driven models. We propose a unifying set of concepts by which to consider and develop place recognition mechanisms and identify key challenges such as generalization, robustness, and environmental variability. This review aims to foster innovations in artificial localization by connecting future developments in artificial place recognition systems to insights from both animal navigation research and human spatial cognition studies.

cs.RO↗

arXiv2025

Pixi: Unified Software Development and Distribution for Robotics and AI

The reproducibility crisis in scientific computing constrains robotics research. Existing studies reveal that up to 70% of robotics algorithms cannot be reproduced by independent teams, while many others fail to reach deployment because creating shareable software environments remains prohibitively complex. These challenges stem from fragmented, multi-language, and hardware-software toolchains that lead to dependency hell. We present Pixi, a unified package-management framework that addresses these issues by capturing exact dependency states in project-level lockfiles, ensuring bit-for-bit reproducibility across platforms. Its high-performance SAT solver achieves up to 10x faster dependency resolution than comparable tools, while integration of the conda-forge and PyPI ecosystems removes the need for multiple managers. Adopted in over 5,300 projects since 2023, Pixi reduces setup times from hours to minutes and lowers technical barriers for researchers worldwide. By enabling scalable, reproducible, collaborative research infrastructure, Pixi accelerates progress in robotics and AI.

cs.RO↗

arXiv2025

Are All Marine Species Created Equal? Performance Disparities in Underwater Object Detection

Underwater object detection is critical for monitoring marine ecosystems but poses unique challenges, including degraded image quality, imbalanced class distribution, and distinct visual characteristics. Not every species is detected equally well, yet underlying causes remain unclear. We address two key research questions: 1) What factors beyond data quantity drive class-specific performance disparities? 2) How can we systematically improve detection of under-performing marine species? We manipulate the DUO and RUOD datasets to separate the object detection task into localization and classification and investigate the under-performance of the scallop class. Localization analysis using YOLO11 and TIDE finds that foreground-background discrimination is the most problematic stage regardless of data quantity. Classification experiments reveal persistent precision gaps even with balanced data, indicating intrinsic feature-based challenges beyond data scarcity and inter-class dependencies. We recommend imbalanced distributions when prioritizing precision, and balanced distributions when prioritizing recall. Improving under-performing classes should focus on algorithmic advances, especially within localization modules. We publicly release our code and datasets.

cs.CV↗