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Adaptive Camera View Clustering for Fast Incremental Image-based 3D Reconstruction

: Neumann, Kai Alexander
: Kuijper, Arjan; Domajnko, Matevz; Tausch, Reimar

Darmstadt, 2020, 94 pp.
Darmstadt, TU, Bachelor Thesis, 2020
Bachelor Thesis
Fraunhofer IGD ()
Structure-from-Motion (SfM); Photogrammetry; Image clustering; Lead Topic- Digitized Work; Research Line: Computer graphics (CG); Research Line: Computer vision (CV)

Photogrammetry, more precisely image-based 3D reconstruction, is an established method for digitizing cultural heritage sites and artifacts. This method utilizes images from different perspectives to reconstruct the geometry and texture of an object. What images are necessary for a successful reconstruction depends on the size, shape, and complexity of the object. Therefore, an autonomous scanning system for 3D reconstruction requires some kind of feedback during acquisition. In this thesis, we present an evaluation of different state-of-the-art photogrammetry solutions to identify which of them is most capable of providing feedback that predicts the quality of the final 3D reconstruction during acquisition. For this, we focused on the open-source incremental reconstruction solutions COLMAP, Alicevision Meshroom and MVE. Additionally, we included the commercial solution Agisoft Metashape to evaluate how it compares against the open-source solutions. While we were able to identify some characteristic behaviors, the accuracy and runtime of all four reconstruction solutions vary based on the input dataset. Because of this, and the fact that all four solutions compute very similar results under the same conditions, our tests were not conclusive. Nevertheless, we chose COLMAP as the back-end for further use as it provided good results on the real dataset as well as an extensive command-line interface (CLI). Based on these results, we introduce an iterative image-based reconstruction pipeline that uses a cluster-based acceleration structure to deliver more robust and efficient 3D reconstructions. The photogrammetry solution used for reconstruction is exchangeable. In this pipeline, images that portray common parts of an object are assigned to clusters based on their camera frustums. Each cluster can be reconstructed separately. The pipeline was implemented as a c++ module and tested on the autonomous robotic scanner CultArm3D®. For this system, we embedded the pipeline in a feedback loop with a density-based Next-Best-View (NBV) algorithm to assist during autonomous acquisition.