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Found 7 papers, 0 papers with code
SLAM for Indoor Mapping of Wide Area Construction Environments
We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment.
Depth Supervised Neural Surface Reconstruction from Airborne Imagery
While originally developed for novel view synthesis, Neural Radiance Fields (NeRFs) have recently emerged as an alternative to multi-view stereo (MVS).
HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields
For global consistency, we propose an efficient Sim(3)-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift.
Juggling With Representations: On the Information Transfer Between Imagery, Point Clouds, and Meshes for Multi-Modal Semantics
By these means, we achieve to enrich feature vectors to multi-modal feature vectors for each representation.
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Automated semantic segmentation and object detection are of great importance in geospatial data analysis.
Photometric Multi-View Mesh Refinement for High-Resolution Satellite Images
Modern high-resolution satellite sensors collect optical imagery with ground sampling distances (GSDs) of 30-50cm, which has sparked a renewed interest in photogrammetric 3D surface reconstruction from satellite data.
Learning Super-resolved Depth from Active Gated Imaging
Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usually restricted to low resolution because of technology or cost limitations.
