Tootfinder

Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action
Victoire Herv\'e, Henrik Warpefelt, Christoph Salge
https://arxiv.org/abs/2509.19030 https://

Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action
Algorithmic evaluation of procedurally generated content struggles to find metrics that align with human experience, particularly for composite artefacts. Automatic decomposition as a possible solution requires concepts that meet a range of properties. To this end, drawing on Games Studies and Game AI research, we introduce the nested concepts of \textit{Landmarks}, \textit{Monuments}, and \textit{Beacons}. These concepts are based on the artefact's perceivability, evocativeness, and Call to Ac…

GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks
Ali Emre Balc{\i} (TU Delft), Erhan Ege Keyvan (Middle East Technical University), Emre \"Ozkan (Middle East Technical University)
https://arxiv.org/abs/2508.16459

GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks
We present a novel Simultaneous Localization and Mapping (SLAM) method that employs Gaussian Process (GP) based landmark (object) representations. Instead of conventional grid maps or point cloud registration, we model the environment on a per object basis using GP based contour representations. These contours are updated online through a recursive scheme, enabling efficient memory usage. The SLAM problem is formulated within a fully Bayesian framework, allowing joint inference over the robot p…

Talking Head Generation via AU-Guided Landmark Prediction
Shao-Yu Chang, Jingyi Xu, Hieu Le, Dimitris Samaras
https://arxiv.org/abs/2509.19749 https://arxi…

Talking Head Generation via AU-Guided Landmark Prediction
We propose a two-stage framework for audio-driven talking head generation with fine-grained expression control via facial Action Units (AUs). Unlike prior methods relying on emotion labels or implicit AU conditioning, our model explicitly maps AUs to 2D facial landmarks, enabling physically grounded, per-frame expression control. In the first stage, a variational motion generator predicts temporally coherent landmark sequences from audio and AU intensities. In the second stage, a diffusion-base…

Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Robert Graf, Tanja Lerchl, Kati Nispel, Hendrik M\"oller, Matan Atad, Julian McGinnis, Julius Maria Watrinet, Johannes Paetzold, Daniel Rueckert, Jan S. Kirschke
https://arxiv.org/abs/2508.14708

Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Digital twins offer a powerful framework for subject-specific simulation and clinical decision support, yet their development often hinges on accurate, individualized anatomical modeling. In this work, we present a rule-based approach for subpixel-accurate key-point extraction from MRI, adapted from prior CT-based methods. Our approach incorporates robust image alignment and vertebra-specific orientation estimation to generate anatomically meaningful landmarks that serve as boundary conditions …

Landmark-Assisted Monte Carlo Planning
David H. Chan, Mark Roberts, Dana S. Nau
https://arxiv.org/abs/2508.11493 https://arxiv.org/pdf/2508.11493

Landmark-Assisted Monte Carlo Planning
Landmarks$\unicode{x2013}$conditions that must be satisfied at some point in every solution plan$\unicode{x2013}$have contributed to major advancements in classical planning, but they have seldom been used in stochastic domains. We formalize probabilistic landmarks and adapt the UCT algorithm to leverage them as subgoals to decompose MDPs; core to the adaptation is balancing between greedy landmark achievement and final goal achievement. Our results in benchmark domains show that well-chosen la…

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Jiahui An, Bingjie Cheng, Dmitriy Rudyka, Elisa Donati, Sara Fabrikant
https://arxiv.org/abs/2509.14056

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify cognitive load during map-based wayfinding and whether classification accuracy depends more on task complexity or on individual traits. EEG recordings from forty-six participants navigating routes with 3, 5, or 7 map landmarks were analyzed with a nested cross…

An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection
Yung-Chen (Hailey), Hsieh, Yu Sun
https://arxiv.org/abs/2508.11683

An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection
Poor posture has become an increasingly prevalent concern due to students and workers spending extended amounts of time sitting at a desk. To address this issue, we developed PoseTrack, a mobile application that uses a Raspberry Pi Camera and Mediapipe Pose landmarks to monitor the userś posture and provide real time feedback. The system detects poor posture, including forward lean, slouching, hunched shoulders, crossed legs, etc. Some challenges we faced were obtaining posture data, transferr…

Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation
Sebastian Diaz, Benjamin Billot, Neel Dey, Molin Zhang, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson
https://arxiv.org/abs/2509.12062

Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation
Fetal motion is a critical indicator of neurological development and intrauterine health, yet its quantification remains challenging, particularly at earlier gestational ages (GA). Current methods track fetal motion by predicting the location of annotated landmarks on 3D echo planar imaging (EPI) time-series, primarily in third-trimester fetuses. The predicted landmarks enable simplification of the fetal body for downstream analysis. While these methods perform well within their training age di…

Continuing a bit with the #photos from our #schwarzwald #vacations : (unknowingly) we walked along the Western-way. It was pretty cool because the trail was very diverse as you can see on the photo…

I have been traveling through Greece with a rental car last week, really recommended, mixing little villages along the coastline with classic landmarks like Delphi and Epidaurus. Also low prices compared with the Netherlands.
#greece #holiday

Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances
Issa Hanou, Sebastijan Duman\v{c}i\'c, Mathijs de Weerdt
https://arxiv.org/abs/2508.21564

Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances
We propose a new framework for discovering landmarks that automatically generalize across a domain. These generalized landmarks are learned from a set of solved instances and describe intermediate goals for planning problems where traditional landmark extraction algorithms fall short. Our generalized landmarks extend beyond the predicates of a domain by using state functions that are independent of the objects of a specific problem and apply to all similar objects, thus capturing repetition. Ba…

"Famous UK landmarks at risk from climate damage, Aviva warns"
#UK #UnitedKingdom #Climate #ClimateChange

Introspection in Learned Semantic Scene Graph Localisation
Manshika Charvi Bissessur, Efimia Panagiotaki, Daniele De Martini
https://arxiv.org/abs/2510.07053 https://

Introspection in Learned Semantic Scene Graph Localisation
This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. …

Good Morning #Canada
I think most Canadians know what an #Inukshuk is, but this post is intended to give you a greater understanding of these iconic piles of rock. An inuksuk, or inukshuk (plural inuksuit) is a type of stone landmark or cairn built and used by Inuit, including Iñupiat, Kalaallit, Yupik, and other peoples of the North American Arctic. These structures are found in northern Canada, north of the Arctic Circle, which is dominated by the tundra biome and has areas with few natural landmarks. These structures, often resembling human figures, serve various purposes, primarily as navigational aids and markers, but also as symbols of the Arctic landscape and Inuit culture.
#CanadaIsAwesome #Inuit #Sculpture
https://thecanadianencyclopedia.ca/en/article/inuksuk-inukshuk

PianoVAM: A Multimodal Piano Performance Dataset
Yonghyun Kim, Junhyung Park, Joonhyung Bae, Kirak Kim, Taegyun Kwon, Alexander Lerch, Juhan Nam
https://arxiv.org/abs/2509.08800

PianoVAM: A Multimodal Piano Performance Dataset
The multimodal nature of music performance has driven increasing interest in data beyond the audio domain within the music information retrieval (MIR) community. This paper introduces PianoVAM, a comprehensive piano performance dataset that includes videos, audio, MIDI, hand landmarks, fingering labels, and rich metadata. The dataset was recorded using a Disklavier piano, capturing audio and MIDI from amateur pianists during their daily practice sessions, alongside synchronized top-view videos …

Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations
Wouter Jansen, Jan Steckel
https://arxiv.org/abs/2509.06615 https://

Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations
In environments where visual sensors falter, in-air sonar provides a reliable alternative for autonomous systems. While previous research has successfully classified individual acoustic landmarks, this paper takes a step towards increasing information capacity by introducing reflector constellations as encoded tags. Our primary contribution is a multi-label Convolutional Neural Network (CNN) designed to simultaneously identify multiple, closely spaced reflectors from a single in-air 3D sonar me…

Gaussian Semantic Field for One-shot LiDAR Global Localization
Pengyu Yin, Shenghai Yuan, Haozhi Cao, Xingyu Ji, Ruofei Bai, Siyu Chen, Lihua Xie
https://arxiv.org/abs/2510.12101

Gaussian Semantic Field for One-shot LiDAR Global Localization
We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a lightweight tri-layered scene graph. While landmark semantic registration-based methods have shown promising performance improvements in global localization compared with geometric-only methods, landmarks can be repetitive and misleading for correspondence establishment. We propose to mitigate this problem by modeling semantic distributions with continuous functions learned from a popu…

PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization
Ali Shadman Yazdi, Annalisa Cappella, Benedetta Baldini, Riccardo Solazzo, Gianluca Tartaglia, Chiarella Sforza, Giuseppe Baselli
https://arxiv.org/abs/2510.00910

PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization
Manual annotation of anatomical landmarks on 3D facial scans is a time-consuming and expertise-dependent task, yet it remains critical for clinical assessments, morphometric analysis, and craniofacial research. While several deep learning methods have been proposed for facial landmark localization, most focus on pseudo-landmarks or require complex input representations, limiting their clinical applicability. This study presents a fully automated deep learning pipeline (PAL-Net) for localizing 5…

Taking Language Embedded 3D Gaussian Splatting into the Wild
Yuze Wang, Yue Qi
https://arxiv.org/abs/2507.19830 https://arxiv.org/pdf/2507.19830

Taking Language Embedded 3D Gaussian Splatting into the Wild
Recent advances in leveraging large-scale Internet photo collections for 3D reconstruction have enabled immersive virtual exploration of landmarks and historic sites worldwide. However, little attention has been given to the immersive understanding of architectural styles and structural knowledge, which remains largely confined to browsing static text-image pairs. Therefore, can we draw inspiration from 3D in-the-wild reconstruction techniques and use unconstrained photo collections to create a…

A Multi-view Landmark Representation Approach with Application to GNSS-Visual-Inertial Odometry
Tong Hua, Jiale Han, Wei Ouyang
https://arxiv.org/abs/2508.05368 https://

A Multi-view Landmark Representation Approach with Application to GNSS-Visual-Inertial Odometry
Invariant Extended Kalman Filter (IEKF) has been a significant technique in vision-aided sensor fusion. However, it usually suffers from high computational burden when jointly optimizing camera poses and the landmarks. To improve its efficiency and applicability for multi-sensor fusion, we present a multi-view pose-only estimation approach with its application to GNSS-Visual-Inertial Odometry (GVIO) in this paper. Our main contribution is deriving a visual measurement model which directly assoc…

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Spatio-Temporal-Network Point Processes for Modeling Crime Events with Landmarks
Zheng Dong, Jorge Mateu, Yao Xie

Monocular Vision-Based Swarm Robot Localization Using Equilateral Triangular Formations
Taewon Kang, Ji-Wook Kwon, Il Bae, Jin Hyo Kim
https://arxiv.org/abs/2507.19100 https://

Monocular Vision-Based Swarm Robot Localization Using Equilateral Triangular Formations
Localization of mobile robots is crucial for deploying robots in real-world applications such as search and rescue missions. This work aims to develop an accurate localization system applicable to swarm robots equipped only with low-cost monocular vision sensors and visual markers. The system is designed to operate in fully open spaces, without landmarks or support from positioning infrastructures. To achieve this, we propose a localization method based on equilateral triangular formations. By …