Efficient image-space extraction and representation of 3D surface topography

Zeppelzauer, Matthias; Seidl, Markus

doi:10.1109/icip.2015.7351322

Cited by 9 publications

(16 citation statements)

References 18 publications

(22 reference statements)

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“…This normalization can be efficiently performed in a pre-processing step by subtracting a smoothed version of the depth map (Gaussian filter with σ = 12.5 mm) from the depth map. This operation results in a local constrast equalization across the depth map [12] that better enhances the fine geometric details of the surface texture.…”

Section: Baseline Resultsmentioning

confidence: 99%

“…Second, we apply Convolutional Neural Networks (CNNs) [22], [23], which currently show best performance on standard semantic segmentation benchmarks [17]- [19], [24], [25] and compare them with the RF-based approach. We have shown previously that surface segmentation with 3D descriptors computed directly from the 3D point clouds is computationally demanding and with current state-of-the-art methods not performing well, see [12] for respective results for a subset of our dataset. Hence, we employ the depth maps and orthophotos generated from the point clouds as input to segmentation.…”

Section: B Methodsmentioning

confidence: 99%

“…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:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The 3D-Pitoti Dataset

Poier

Seidl

Zeppelzauer

et al. 2017

Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing

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Abstract-The development of powerful 3D scanning hardware and reconstruction algorithms has strongly promoted the generation of 3D surface reconstructions in different domains. An area of special interest for such 3D reconstructions is the cultural heritage domain, where surface reconstructions are generated to digitally preserve historical artifacts. While reconstruction quality nowadays is sufficient in many cases, the robust analysis (e.g. segmentation, matching, and classification) of reconstructed 3D data is still an open topic. In this paper, we target the automatic and interactive segmentation of high-resolution 3D surface reconstructions from the archaeological domain. To foster research in this field, we introduce a fully annotated and publicly available large-scale 3D surface dataset including high-resolution meshes, depth maps and point clouds as a novel benchmark dataset to the community. We provide baseline results for our existing random forest-based approach and for the first time investigate segmentation with convolutional neural networks (CNNs) on the data. Results show that both approaches have complementary strengths and weaknesses and that the provided dataset represents a challenge for future research.

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Section: Baseline Resultsmentioning

confidence: 99%

Section: B Methodsmentioning

confidence: 99%

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The 3D-Pitoti Dataset

Poier

Seidl

Zeppelzauer

et al. 2017

Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing

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show abstract

“…Next, we enhance the depth map to extract and emphasize the fine geometric structures related to the peck-marks that make up petroglyph shapes with the method of Zeppelzauer & Seidl [21]. A classifier is trained on the enhanced map that generates probabilities for each pixel.…”

Section: D Segmentation Approachmentioning

confidence: 99%

“…3(b)). To enhance local structures of peck-marks we locally filter the deviation map by a Gaussian filter, see [21] for a detailed description. Locations where the filter matches well (e.g.…”

Section: A Data Preprocessing and Enhancementmentioning

confidence: 99%

Interactive segmentation of rock-art in high-resolution 3D reconstructions

Zeppelzauer

Poier

Seidl

et al. 2015

2015 Digital Heritage

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Petroglyphs (rock engravings) are important artifacts for the documentation and analysis of early human life. Recent improvements in 3D scanning and 3D reconstruction enable the accurate 3D reconstruction of petroglyphs from rock surfaces at sub-millimeter resolution. To enable the indexing, matching, and recognition of petroglyphs in petroglyph databases, the shapes must first be segmented from the reconstructed rock surface. The absence of robust 3D segmentation methods for petroglyphs leaves a gap in the digital processing workflow. In this paper, we present a semi-automatic method for petroglyph segmentation for high-resolution 3D surface reconstructions. A comprehensive evaluation shows that our method is able to robustly segment petroglyphs with high accuracy and that the incorporation of 3D information is crucial to solve the segmentation problem. The presented method represents a major step towards the completion of a full 3D digital processing workflow of petroglyphs.

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Rock Art in Three Dimensions: Comments on the Use and Possibilities of 3D Rock Art Documentation

Horn,

Peternell,

Ling

et al. 2024

The 3 Dimensions of Digitalised Archaeology

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Methods to document rock art in all three dimensions have become a standardized workflow. In this article, we discuss their advantages and disadvantages when compared to older reductive approaches to rock art documentation. Furthermore, some misunderstandings regarding 3D documentation are addressed. As the majority of the problems presented by the 3D documentation of rock art can be solved through advanced visualization workflows, recent developments in this area are described. The rock art documentation described in this contribution also serves wider research purposes, which will be discussed. Newly discovered images and newly developed machine learning algorithms will also be introduced.

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Efficient image-space extraction and representation of 3D surface topography

Cited by 9 publications

References 18 publications

The 3D-Pitoti Dataset

The 3D-Pitoti Dataset

Interactive segmentation of rock-art in high-resolution 3D reconstructions

Rock Art in Three Dimensions: Comments on the Use and Possibilities of 3D Rock Art Documentation

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