Scene Coordinate Regression

Latest papers

8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

G $^2$ WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation
Oct 12, 2026Ke Gui, Chenghao Shi, Ziyue Wang +3Scene Coordinate Regression Image-Goal Navigation
College of Intelligence Science and Technology, National University of Defense Technology, China · National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China · School of Future Technology, National University of Defense Technology, China
ReLoc: Rethinking Scene Coordinate Regression Architecture for Robust Outdoor LiDAR-based Localization
Sep 27, 2026Heejoon Moon, Yurim Cho, Je Hyeong HongScene Coordinate Regression Point Cloud Learning
Department of Artificial Intelligence, Hanyang University · Department of Electronic Engineering, Hanyang University
LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization
Aug 15, 2026Wen Li, Shangshu Yu, Dunqiang Liu +6Scene Coordinate Regression LiDAR Localization
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, and with Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen, FJ 361005, P.R. China · School of Engineering Mathematics and Technology, University of Bristol, Bristol BS8 1TH, United Kingdom · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
UQ-Loc: Uncertainty-Aware LiDAR Scene Coordinate Regression
Aug 6, 2026Jacek KomorowskiScene Coordinate Regression Uncertainty Quantification
Warsaw University of Technology, Warsaw, Poland
Seeing Through the Weights: Privacy Leakage in Scene Coordinate Regression
Jun 30, 2026Oleksii Nasypanyi, Jaemin Cho, Utku Ozbulak +2Scene Coordinate Regression 3D Reconstruction
Stony Brook University · Ghent University Global Campus · George Mason University Korea +1
ACEsplat: Accelerated 3D Gaussian Scene Regression via RGB and Poses Only
Jun 20, 2026Mingkai Liu, Haohua Que, Dikai Fan +73D Gaussian Splatting Scene Coordinate Regression
Peking University · University of Georgia · Infinity Robotics +3
MB-Loc: Multi-planar Bird's-eye-view Localization in outdoor LiDAR scenes
Jun 7, 2026Ayaan Choudhury, Preet Savalia, Anirudh Pydah +1Scene Coordinate Regression Bird's-Eye-View Representation
Indian Institute of Technology Jodhpur, India
SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Jul 10, 2025Juyeop Han, Lukas Lao Beyer, Guilherme V. Cavalheiro +1Trajectory Optimization Scene Coordinate Regression
Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Oct 12, 2026J/K move · Enter open · S save

G $^2$ WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation

Ke Gui, Chenghao Shi, Ziyue Wang, Longwen Chen+2

College of Intelligence Science and Technology, National University of Defense Technology, China · National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China · School of Future Technology, National University of Defense Technology, China

Scene Coordinate Regression Image-Goal Navigation World Models for Robotics World Action Models Efficient Inference for World Action Models

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World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies beyond the current field of view, the agent must not only model environmental dynamics but also maintain a persistent spatial memory of the scene. Existing approaches either introduce spatial context by fine-tuning pretrained models on the target task, which underexploits their spatial priors, or rely on external 3D reconstruction, which is computationally expensive for real-world deployment. In this paper, we propose the Geometry Grounded World Action Model (G

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WAM) for image-goal navigation. G

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WAM encodes spatial cues through a learned scene-coordinate localization module that anchors the current, goal, and history frames in a common reference frame, yielding a stable and consistent spatial context that enables efficient navigation without explicit 3D reconstruction. Following the world-action-model paradigm, G

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WAM jointly predicts future states and actions, eliminating the need for costly cross-entropy method (CEM) style planning. In addition, G

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WAM operates entirely in latent space, avoiding the computational burden of pixel-level reconstruction and substantially reducing inference cost. Across four trajectory prediction benchmarks, G

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WAM reduces average ATE and RPE by 40.1% and 35.2%, respectively. In closed-loop navigation experiments, it improves the success rate from 54.0% to 63.0% and the success weighted by path length (SPL) from 36.4% to 59.2%, while maintaining high efficiency.