ATE comparison at 30 FPS and 75 FPS for ORB-SLAM2 (left) and Mesh2SLAM (right).
SLAM is a foundational technique with broad applications in robotics and AR/VR. SLAM simulations evaluate new concepts, but testing on resource-constrained devices, such as VR HMDs, faces challenges: high computational cost and restricted sensor data access. This work proposes a sparse framework using mesh geometry projections as features, which improves efficiency and circumvents direct sensor data access, advancing SLAM research as we demonstrate in VR and through numerical evaluation.
@inproceedings{Sousa2025Mesh2SLAMVRFast,
abstract = {SLAM is a foundational technique with broad applications in robotics and AR/VR. SLAM simulations evaluate new concepts, but testing on resource-constrained devices, such as VR HMDs, faces challenges: high computational cost and restricted sensor data access. This work proposes a sparse framework using mesh geometry projections as features, which improves efficiency and circumvents direct sensor data access, advancing SLAM research as we demonstrate in VR and through numerical evaluation.},
address = {Los Alamitos, CA, USA},
author = {C. A. P. de Sousa, H. Hamann, O. Deussen},
booktitle = {2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
doi = {10.1109/VRW66409.2025.00021},
keywords = {Location awareness;Geometry;Solid modeling;Simultaneous localization and mapping;Three-dimensional displays;Tracking;Computational modeling;Refining;Virtual environments;User interfaces},
month = {March},
pages = {57-62},
publisher = {IEEE Computer Society},
title = { Mesh2SLAM in VR: A Fast Geometry-Based SLAM Framework for Rapid Prototyping in Virtual Reality Applications },
url = {https://doi.ieeecomputersociety.org/10.1109/VRW66409.2025.00021},
year = {2025}
}