Tootfinder

MCOP: Multi-UAV Collaborative Occupancy Prediction
Zefu Lin, Wenbo Chen, Xiaojuan Jin, Yuran Yang, Lue Fan, Yixin Zhang, Yufeng Zhang, Zhaoxiang Zhang
https://arxiv.org/abs/2510.12679

MCOP: Multi-UAV Collaborative Occupancy Prediction
Unmanned Aerial Vehicle (UAV) swarm systems necessitate efficient collaborative perception mechanisms for diverse operational scenarios. Current Bird's Eye View (BEV)-based approaches exhibit two main limitations: bounding-box representations fail to capture complete semantic and geometric information of the scene, and their performance significantly degrades when encountering undefined or occluded objects. To address these limitations, we propose a novel multi-UAV collaborative occupancy predi…

Towards Reliable and Interpretable Document Question Answering via VLMs
Alessio Chen, Simone Giovannini, Andrea Gemelli, Fabio Coppini, Simone Marinai
https://arxiv.org/abs/2509.10129

Towards Reliable and Interpretable Document Question Answering via VLMs
Vision-Language Models (VLMs) have shown strong capabilities in document understanding, particularly in identifying and extracting textual information from complex documents. Despite this, accurately localizing answers within documents remains a major challenge, limiting both interpretability and real-world applicability. To address this, we introduce \textit{DocExplainerV0}, a plug-and-play bounding-box prediction module that decouples answer generation from spatial localization. This design m…

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae
https://arxiv.org/abs/2508.08748

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as color, shape, size, and texture. These factors complicate trajectory planning and grasping. This paper introduces a perception-action pipeline leveraging annotation-guided visual prompting, where bounding box annotations identify both pickable objects and placement locations, providing structured spatial guidance. Instead of traditional ste…

New #rstats https://github.com/e-kotov/gridmaker Creates Eurostat GISCO compatible and INSPIRE-compliant grids with IDs that look like ‘CRS3035RES1000mN3497000E4448000’ or ‘1kmN3497E4447’. Input can be sf, or …

Improve bounding box in Carla Simulator
Mohamad Mofeed Chaar, Jamal Raiyn, Galia Weidl
https://arxiv.org/abs/2509.16773 https://arxiv.org/pdf/2509.16773

Improve bounding box in Carla Simulator
The CARLA simulator (Car Learning to Act) serves as a robust platform for testing algorithms and generating datasets in the field of Autonomous Driving (AD). It provides control over various environmental parameters, enabling thorough evaluation. Development bounding boxes are commonly utilized tools in deep learning and play a crucial role in AD applications. The predominant method for data generation in the CARLA Simulator involves identifying and delineating objects of interest, such as vehi…

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision
Safouane El Ghazouali, Umberto Michelucci
https://arxiv.org/abs/2509.04180 https://

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision
AI models rely on annotated data to learn pattern and perform prediction. Annotation is usually a labor-intensive step that require associating labels ranging from a simple classification label to more complex tasks such as object detection, oriented bounding box estimation, and instance segmentation. Traditional tools often require extensive manual input, limiting scalability for large datasets. To address this, we introduce VisioFirm, an open-source web application designed to streamline imag…

V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task
Jikai Chen, Long Chen, Dong Wang, Leilei Gan, Chenyi Zhuang, Jinjie Gu
https://arxiv.org/abs/2508.13634

V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interaction uncertainty and visual-semantic hierarchies. Recent methods incorporate attention mechanisms but still face two key issues: (1) ignoring processing background regions causes attention drift from the desired area, and (2) uniform labeling fails to distinguish between center and edges of the target UI element, leadin…

Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Levin M Moser, Ahmad Kamal Hamid, Esteban Miglietta, Nodar Gogoberidze, Beth A Cimini
https://arxiv.org/abs/2507.22212

Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Spatial transcriptomics (ST) technologies not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stage for integration with other imaging-based modalities. However, how to best exploit spatial context and integrate ST with imaging-based modalities remains an open question. To address this, particularly under real-world experimental constraints such as limited dataset size, class imbalance, and bounding-box-based segm…

Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
Jan Krej\v{c}\'i, Oliver Kost, Yuxuan Xia, Lennart Svensson, Ond\v{r}ej Straka
https://arxiv.org/abs/2508.13647

Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard point-object (SPO) model is adopted, and the posterior density is computed using the Poisson multi-Bernoulli mixture (PMBM) filter. The selection of the model parameters rooted in continuous time is discussed, including the birth and survival probabilities. Some parameters are selected from the first principles, while …

Perspective-Invariant 3D Object Detection
Ao Liang, Lingdong Kong, Dongyue Lu, Youquan Liu, Jian Fang, Huaici Zhao, Wei Tsang Ooi
https://arxiv.org/abs/2507.17665 https://

Perspective-Invariant 3D Object Detection
With the rise of robotics, LiDAR-based 3D object detection has garnered significant attention in both academia and industry. However, existing datasets and methods predominantly focus on vehicle-mounted platforms, leaving other autonomous platforms underexplored. To bridge this gap, we introduce Pi3DET, the first benchmark featuring LiDAR data and 3D bounding box annotations collected from multiple platforms: vehicle, quadruped, and drone, thereby facilitating research in 3D object detection fo…

RadarGaussianDet3D: An Efficient and Effective Gaussian-based 3D Detector with 4D Automotive Radars
Weiyi Xiong, Bing Zhu, Tao Huang, Zewei Zheng
https://arxiv.org/abs/2509.16119

RadarGaussianDet3D: An Efficient and Effective Gaussian-based 3D Detector with 4D Automotive Radars
4D automotive radars have gained increasing attention for autonomous driving due to their low cost, robustness, and inherent velocity measurement capability. However, existing 4D radar-based 3D detectors rely heavily on pillar encoders for BEV feature extraction, where each point contributes to only a single BEV grid, resulting in sparse feature maps and degraded representation quality. In addition, they also optimize bounding box attributes independently, leading to sub-optimal detection accur…