Interactive 3D Segmentation of Rock-Art by Enhanced Depth Maps and Gradient Preserving Regularization

Abstract

Petroglyphs (rock engravings) have been pecked and engraved by humans into natural rock surfaces thousands of years ago and are among the oldest artifacts that document early human life and culture. Some of these rock engravings have survived until the present and serve today as a unique document of ancient human life. Since petroglyphs are pecked into the surface of natural rocks, they are threatened by environmental factors such as weather and erosion. To document and preserve these valuable artifacts of human history, the 3D digitization of rock surfaces has become a suitable approach due to the development of powerful 3D reconstruction techniques in recent years. The results of 3D reconstruction are huge 3D point clouds which represent the local surface geometry in high resolution. In this article, we present an automatic 3D segmentation approach that is able to extract rock engravings from reconstructed 3D surfaces. To solve this computationally complex problem, we transfer the task of segmentation to the image-space in order to efficiently perform segmentation. Adaptive learning is applied to realize interactive segmentation and a gradient preserving energy minimization assures smooth boundaries for the segmented figures. Our experiments demonstrate the efficiency and the strong segmentation capabilities of the approach. The precise segmentation of petroglyphs from 3D surfaces provides the foundation for compiling large petroglyph databases which can then be indexed and searched automatically.

The 3D-Pitoti Dataset

“…This paper builds upon a series of incremental previous works on 3D surface segmentation and classification [12]- [14] and intends to consolidate and extend the achieved results. Our contribution beyond previous research are as follows:…”

“…In our case the segmentation task is a pixelwise binary problem and, hence, the evaluation is based on the predicted segmentation mask and the ground truth mask. Based on these masks we compute the Jaccard index [16], also often termed region based intersection over union (IU ), for which we compute the average over classes (mIU ) as in [17]- [20], the pixel accuracy (PA) [20], [21], the dice similarity coefficient (DSC) [14], the hit rate (HR) [14], [20] and the false acceptance rate (F AR) [14].…”

“…• We provide baselines for our existing approach [13], [14] and a novel CNN-based approach to enable instant performance comparisons.…”

Using Structure from Motion Mapping to Record and Analyze Details of the Colossal Hats (Pukao) of Monumental Statues on Rapa Nui (Easter Island)

“…SfM is well-suited for identifying petroglyphs on uneven surfaces, such as those often found in caves (Caninas et al 2016; Fritz et al 2016), and SfM models give spatial context to petroglyphs that is helpful for interpretation (Alexander et al 2015; Janik et al 2007). Because of the value of SfM for the interpretation of rock art, archaeologists have recently explored methods of digitally enhancing model visualization (e.g., Carrero-Pazos et al 2016; Vilas-Estevez et al 2016), have developed specialized tools for efficiently collecting rock art photographs (e.g., Höll et al 2014), and have segmented rock art models to effectively store and query models in databases (e.g., Poier et al 2016; Zeppelzauer et al 2015; Zeppelzauer et al 2016). Significantly, SfM mapping has a substantially lower impact on rock art than tracing, and monitoring of archaeological features through SfM mapping can be used to identify conservation priorities (Plets et al 2012).…”

Agent‐oriented and ontology‐driven digital libraries: the IndianaMAS experience

“…It is based on the other major alpine rock art site besides Mount Bego, thus complementing our work from a geographical point of view; Valcamonica rock engravings have been produced in a wider time span than Mount Bego ones, thus giving more complete information of the lifestyle of the ancient (but also of the most recent) people and complementing the prehistorical information coming from Mount Bego, which mainly refers to the Bronze Age. From a scientific point of view, the image acquisition and recognition hardware and software tools developed during the 3D Pitoti project are extremely precise and efficient ones , but they are not driven by any common knowledge representation. As far as the multilingual text analysis and classification are concerned, they have not been addressed into the 3D Pitoti project; also, data are stored into a traditional database, whereas we experimented the integration of a standard DL into our system.…”