In this paper, we present a 3D reconstruction and enhancement approach for high quality dynamic city scene reconstructions. We first detect and segment the moving objects using 3D Motion Segmentation approach by exploiting the feature trajectories’ behaviours. Getting the segmentations of both the dynamic scene parts and the static scene parts, we propose an efficient point cloud registration approach which takes the advantages of 3-point RANSAC and Iterative Closest Points algorithms to produce precise point cloud alignment. Furthermore, we proposed a point cloud smoothing and texture mapping framework to enhance the results of reconstructions for both the static and the dynamic scene parts. The proposed algorithms are evaluated using the real-world challenging KITTI dataset with very satisfactory results.
CITATION STYLE
Jiang, C., Fougerolle, Y., Fofi, D., & Demonceaux, C. (2017). Dynamic 3D scene reconstruction and enhancement. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10484 LNCS, pp. 518–529). Springer Verlag. https://doi.org/10.1007/978-3-319-68560-1_46
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