# On-the-fly Feedback SfM: Online Explore-and-Exploit UAV Photogrammetry with Incremental Mesh Quality-Aware Indicator and Predictive Path Planning

arXiv: https://arxiv.org/abs/2512.02375 · HTML: https://mlsanity.com/paper/2512.02375/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset](https://mlsanity.com/paper/2107.04286/) (2021)
- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao](https://mlsanity.com/paper/2206.12216/) (2022)
- [PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction](https://mlsanity.com/paper/2409.05474/) (2024)
- [UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction](https://mlsanity.com/paper/1605.01923/) (2016)
- [Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction](https://mlsanity.com/paper/2307.05832/) (2023)

## Built on by (most cited first, 1 of 1)

- [OpenFlyScan: A Quality-Guided Aerial Reconstruction System for Consumer Drones](https://mlsanity.com/paper/2609.24253/) (2026)

## Abstract (arXiv)

Compared with conventional offline UAV photogrammetry, real-time UAV photogrammetry is essential for time-critical geospatial applications such as disaster response and active digital-twin maintenance. However, most existing methods focus on processing captured images or sequential frames in real time, without explicitly evaluating the quality of the on-the-go 3D reconstruction or providing guided feedback to enhance image acquisition in the target area. This work presents On-the-fly Feedback SfM, an explore-and-exploit framework for real-time UAV photogrammetry, enabling iterative exploration of unseen regions and exploitation of already observed and reconstructed areas in near real time. Built upon SfM on-the-fly , the proposed method integrates three modules: (1) online incremental coarse-mesh generation for dynamically expanding sparse 3D point cloud; (2) online mesh quality assessment with actionable indicators; and (3) predictive path planning for on-the-fly trajectory refinement. Comprehensive experiments demonstrate that our method achieves in-situ reconstruction and evaluation in near real time while providing actionable feedback that markedly reduces coverage gaps and re-flight costs. Via the integration of data collection, processing, 3D reconstruction and assessment, and online feedback, our on the-fly feedback SfM could be an alternative for the transition from traditional passive working mode to a more intelligent and adaptive exploration workflow. Code is now available at https://github.com/IRIS-LAB-whu/OntheflySfMFeedback.