Abstract:
This paper introduces an efficient method for 3D instance segmentation based
on 2D object detection, applied to the photogrammetric survey images of archaeological
sites. The method capitalizes on the relationship between the 3D
model and the set of 2D images utilized to compute it. 2D detections on the
images are projected and transformed into a 3D instance segmentation, thus
identifying unique objects within the scene. The primary contribution of this
work is the development of a semi-automatic image annotation method, augmented
by an object tracking technique that leverages the temporal continuity
of image sequences. Additionally, a novel ad-hoc evaluation process has been
integrated into the conventional annotation-training-testing cycle to determine
the necessity of additional annotations. This process tests the consistency of the
3D objects yielded by the 2D detection. The efficacy of the proposed method has
been validated on the underwater site of Xlendi in Malta, resulting in complete
and accurate 3D instance segmentation. Compared to traditional methods, the
object tracking approach adopted has facilitated a 90% reduction in the need
for manual annotations, The approach streamlines precise 3D detection, establishing
a robust foundation for comprehensive 3D instance segmentation. This
enhancement enriches the 3D survey, providing profound insights and facilitat-ing seamless exploration of the Xlendi site from an archaeological perspective.
