Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting
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Bibliographic record
Abstract
Abstract Keeping an up-to-date three-dimensional (3D) representation of buildings is a crucial yet time-consuming step for Building Information Modeling (BIM) and digital twins. To address this issue, we propose ICON (Intelligent CONstruction) drone, an unmanned aerial vehicle (UAV) designed to navigate indoor environments autonomously and generate point clouds. ICON drone is constructed using a 250 mm quadcopter frame, a Pixhawk flight controller, and is equipped with an onboard computer, an Red Green Blue-Depth camera and an IMU (Inertial Measurement Unit) sensor. The UAV navigates autonomously using visual-inertial odometer and frontier-based exploration. The collected RGB images during the flight are used for 3D reconstruction and semantic segmentation. To improve the reconstruction accuracy in weak-texture areas in indoor environments, we propose depth-regularized planar-based Gaussian splatting reconstruction, where we use monocular-depth estimation as extra supervision for weak-texture areas. The final outputs are point clouds with building components and material labels. We tested the UAV in three scenes in an educational building: the classroom, the lobby, and the lounge. Results show that the ICON drone could: (1) explore all three scenes autonomously, (2) generate absolute scale point clouds with F1-score of 0.5806, 0.6638, and 0.8167 compared to point clouds collected using a high-fidelity terrestrial LiDAR scanner, and (3) label the point cloud with corresponding building components and material with mean intersection over union of 0.588 and 0.629. The reconstruction algorithm is further evaluated on ScanNet, and results show that our method outperforms previous methods by a large margin on 3D reconstruction quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it