Automatic 3D Object Fracturing for Evaluation of Partial Retrieval and Object Restoration Tasks : Benchmark and Application to 3D Cultural Heritage Data
2015, Gregor, Robert, Bauer, Danny, Sipiran, Ivan, Perakis, Panagiotis, Schreck, Tobias
Recently, 3D digitization and printing hardware have seen rapidly increasing adoption. High-quality digitization of real-world objects is becoming more and more efficient. In this context, growing amounts of data from the cultural heritage (CH) domain such as columns, tombstones or arches are being digitized and archived in 3D repositories. In many cases, these objects are not complete, but fragmented into several pieces and eroded over time. As manual restoration of fragmented objects is a tedious and error-prone process, recent work has addressed automatic reassembly and completion of fragmented 3D data sets. While a growing number of related techniques are being proposed by researchers, their evaluation currently is limited to smaller numbers of high-quality test fragment sets. We address this gap by contributing a methodology to automatically generate 3D fragment data based on synthetic fracturing of 3D input objects. Our methodology allows generating large-scale fragment test data sets from existing CH object models, complementing manual benchmark generation based on scanning of fragmented real objects. Besides being scalable, our approach also has the advantage to come with ground truth information (i.e. the input objects), which is often not available when scans of real fragments are used. We apply our approach to the Hampson collection of digitized pottery objects, creating and making available a first, larger restoration test data set that comes with ground truth. Furthermore, we illustrate the usefulness of our test data for evaluation of a recent 3D restoration method based on symmetry analysis and also outline how the applicability of 3D retrieval techniques could be evaluated with respect to 3D restoration tasks. Finally, we discuss first results of an ongoing extension of our methodology to include object erosion processes by means of a physiochemical model simulating weathering effects.
Scalability of Non-Rigid 3D Shape Retrieval
2015, Sipiran, Ivan, Bustos, Benjamin, Schreck, Tobias, Bronstein, Alex, Castellani, Umberto, Choi, Sungbin, Lai, Long, Li, Haisheng, Litman, Roee, Sun, Li
Due to recent advances in 3D acquisition and modeling, increasingly large amounts of 3D shape data become available in many application domains. This rises not only the need for effective methods for 3D shape retrieval, but also efficient retrieval and robust implementations. Previous 3D retrieval challenges have mainly considered data sets in the range of a few thousands of queries. In the 2015 SHREC track on Scalability of 3D Shape Retrieval we provide a benchmark with more than 96 thousand shapes. The data set is based on a non-rigid retrieval benchmark enhanced by other existing shape benchmarks. From the baseline models, a large set of partial objects were automatically created by simulating a range-image acquisition process. Four teams have participated in the track, with most methods providing very good to near-perfect retrieval results, and one less complex baseline method providing fair performance. Timing results indicate that three of the methods including the latter baseline one provide near- interactive time query execution. Generally, the cost of data pre-processing varies depending on the method.
Towards Automated 3D Reconstruction of Cultural Heritage Objects
2014, Gregor, Robert, Sipiran, Ivan, Papaioannou, Georgios, Schreck, Tobias, Andreadis, Anthousis, Mavridis, Pavlos
Due to recent improvements in 3D acquisition and shape processing technology, the digitization of Cultural Heritage (CH) artifacts is gaining increased application in context of archival and archaeological research. This increasing availability of acquisition technologies also implies a need for intelligent processing methods that can cope with imperfect object scans. Specifically for Cultural Heritage objects, besides imperfections given by the digitization process, also the original artifact objects may be imperfect due to deterioration or fragmentation processes. Currently, the reconstruction of previously digitized CH artifacts is mostly performed manually by expert users reassembling fragment parts and completing imperfect objects by modeling. However, more automatic methods for CH object repair and completion are needed to cope with increasingly large data becoming available. In this conceptual paper, we first provide a brief survey of typical imperfections in CH artifact scan data and in turn motivate the need for respective repair methods. We survey and classify a selection of existing reconstruction methods with respect to their applicability for CH objects, and then discuss how these approaches can be extended and combined to address various types of physical defects that are encountered in CH artifacts by proposing a flexible repair workflow for 3D digitizations of CH objects. The workflow accommodates an automatic reassembly step which can deal with fragmented input data. It also includes the similarity-based retrieval of appropriate complementary object data which is used to repair local and global object defects. Finally, we discuss options for evaluation of the effectiveness of such a CH repair workflow.
Guiding the Exploration of Scatter Plot Data Using Motif-Based Interest Measures
2015, Shao, Lin, Schleicher, Timo, Behrisch, Michael, Schreck, Tobias, Sipiran, Ivan, Keim, Daniel A.
Finding interesting patterns in large scatter plot spaces is a challenging problem and becomes even more difficult with increasing number of dimensions. Previous approaches for exploring large scatter plot spaces like e.g., the well-known Scagnostics approach, mainly focus on ranking scatter plots based on their global properties. However, often local patterns contribute significantly to the interestingness of a scatter plot. We are proposing a novel approach for the automatic determination of interesting views in scatter plot spaces based on analysis of local scatter plot segments. Specifically, we automatically classify similar local scatter plot segments, which we call scatter plot motifs. Inspired by the well-known tf-idf approach from information retrieval, we compute local and global quality measures based on certain frequency properties of the local motifs. We show how we can use these to filter, rank and compare scatter plots and their incorporated motifs. We demonstrate the usefulness of our approach with synthetic and real-world data sets and showcase our corresponding data exploration tool that visualizes the distribution of local scatter plot motifs in relation to a large overall scatter plot space.
Quality Metrics Driven Approach to Visualize Multidimensional Data in Scatterplot Matrix
2014, Behrisch, Michael, Shao, Lin, Kwon, Bum Chul, Schreck, Tobias, Sipiran, Ivan, Keim, Daniel A.
Extracting meaningful information out of vast amounts of highdimensional data is very difficult. Prior research studies have been trying to solve these problems through either automatic data analysis or interactive visualization approaches. Our grand goal is to derive the representative and generalizable quality metrics and to apply the metrics to amplify interesting patterns as well as to mute the uninteresting noise for multidimensional visualizations. In this particular poster, we investigate quality metrics driven approach to achieve the goal for scatterplot matrix (SPLOM). Our main approach is to rearrange scatterplot matrices by sorting scatterplots based upon their patterns especially locally significant ones, called scatterplot motifs. Using the approach, we expect scatterplot matrices to reveal groups of visual patterns appearing adjacent to each other, which helps analysts to gain a clear overview and to delve into specific areas of interest more easily. Our ongoing investigation aims to test and refine the feature vector for scatterplot motifs depending upon data sizes and the number of dimensions.
Empirical evaluation of dissimilarity measures for 3D object retrieval with application to multi-feature retrieval
2015, Gregor, Robert, Lamprecht, Andreas, Sipiran, Ivan, Schreck, Tobias, Bustos, Benjamin
A common approach for implementing content-based multimedia retrieval tasks resorts to extracting high-dimensional feature vectors from the multimedia objects. In combination with an appropriate dissimilarity function, such as the well-known Lp functions or statistical measures like χ2, one can rank objects by dissimilarity with respect to a query. For many multimedia retrieval problems, a large number of feature extraction methods have been proposed and experimentally evaluated for their effectiveness. Much less work has been done to systematically study the impact of the choice of dissimilarity function on the retrieval effectiveness. Inspired by previous work which compared dissimilarity functions for image retrieval, we provide an extensive comparison of dissimilarity measures for 3D object retrieval. Our study is based on an encompassing set of feature extractors, dissimilarity measures and benchmark data sets. We identify the best performing dissimilarity measures and in turn identify dependencies between well-performing dissimilarity measures and types of 3D features. Based on these findings, we show that the effectiveness of 3D retrieval can be improved by a feature-dependent measure choice. In addition, we apply different normalization schemes to the dissimilarity distributions in order to show improved retrieval effectiveness for late fusion of multi-feature combination. Finally, we present preliminary findings on the correlation of rankings for dissimilarity measures, which could be exploited for further improvement of retrieval effectiveness for single features as well as combinations.
Range Scans based 3D Shape Retrieval
2015, Godil, Afzal, Dutagaci, Helin, Bustos, Benjamin, Choi, Sunghyun, Dong, Shuilong, Furuya, Takahiko, Li, Haisheng, Link, Norman, Moriyama, A., Meruane, Rafael, Ohbuchi, Ryutarou, Paulus, Dietrich, Schreck, Tobias, Seib, Viktor, Sipiran, Ivan, Yin, Huanpu, Zhang, Chaoli
The objective of the SHREC'15 Range Scans based 3D Shape Retrieval track is to evaluate algorithms that match range scans of real objects to complete 3D mesh models in a target dataset. The task is to retrieve a rank list of complete 3D models that are of the same category given the range scan of a query object. This capability is essential to many computer vision systems that involves recognition and classification of objects in the environment based on depth information. In this track, the target dataset consists of 1200 3D mesh models and the query set has 180 range scans of 60 physical objects. Six research groups participated in the contest with a total of 16 different runs. This paper presents the track datasets, participants' methods and the results of the contest.
Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices
2014, Shao, Lin, Behrisch, Michael, Schreck, Tobias, Sipiran, Ivan, Kwon, Bum Chul, Keim, Daniel A.
Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.