Indexing 3D Scenes Using the Interaction Bisector Surface

Zhao, Xi; Wang, He; Komura, Taku

doi:10.1145/2574860

Cited by 45 publications

(90 citation statements)

References 43 publications

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“…Yu et al 26 use not only distance information but also the orientation and ergonomic measures between furniture. To quantitatively represent the spatial relationship, especially a complex relationship, Zhao et al 7 and Hu et al 8 use the IBS and its features. In the work of Liu et al, 6 the minimum distance between points on different objects, the distance between bounding box centers, and the ratio between bounding box areas are used as the relative layout descriptor of objects.…”

Section: Related Workmentioning

confidence: 99%

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Building hierarchical structures for 3D scenes based on normalized cut

Zhao

Su²,

Yang³

2018

Computer Animation & Virtual

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The growing number of 3D scene data available online brings in new challenges for scene retrieval, understanding, and synthesis. Traditional shape processing methods have difficulty to manage 3D scenes because such methods ignore the contextual information, that is, the spatial relationship between the objects or groups of objects, which plays a significant role in describing scenes. Therefore, a context-aware representation is needed to deal with such a problem. In this paper, we propose a method to build scene hierarchies based on contextual information. Given a 3D scene, we first use the interaction bisector surface to measure the affinity between different objects/elements of the scene and then apply the normalized cut method to build a hierarchical structure for the whole scene. The resulting hierarchical structure contains not only the relationship between the individual objects but also the relationship between object groups, which provides much richer information of the scene compared with a flat structure that only describes the contacts or affinity between the individual objects. We test our method using several public databases and show that the resulting structure is more consistent with the ground truth. We also show that our method can be used for point cloud segmentation and outperforms previous methods. KEYWORDShierarchical structure, normalized cut, scene analysis, segmentation INTRODUCTIONRecently, an increasing number of 3D scene databases have been available online because of the popularity of depth sensors and user-friendly modeling software. The ability to reuse such databases is critical for many content-hungry applications such as virtual worlds and games. To understand and retrieve existing 3D scene data, or synthesize new scenes based on existing ones, an essential problem is how to encode the scene information with a structural representation, which is compact, organized, and easy to use.Representing 3D scenes effectively is not a trivial task. The 3D scenes can be in a different format, such as depth data, point cloud data, 1 and artificial 3D models. 2 No matter what the format is, the representation should be able to encode not only the geometry of each object but also, more importantly, the contextual information of the scene. The spatial relationship between objects or object groups plays a significant role in describing the scene context. It describes how one object locates relative to another object, that is, A is on top of B, or A hooks on B, and how one object group locates relative to other groups, that is, group A is beside group B, or groups A and B are quite close while group C is a bit far away.

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Section: Related Workmentioning

confidence: 99%

“…Zhao et al 7 use agglomerative hierarchical clustering to merge from the fine details of the scene elements. Zhao et al 7 use agglomerative hierarchical clustering to merge from the fine details of the scene elements.…”

Section: Related Workmentioning

confidence: 99%

Building hierarchical structures for 3D scenes based on normalized cut

Zhao

Su²,

Yang³

2018

Computer Animation & Virtual

Self Cite

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“…For example, one cannot reliably determine the object enclosure relationship based on a bounding box. Recently, He et al [ZWK14] propose to use biologically-inspired bisector surface to characterize the geometric interaction between adjacent objects and index 3D scenes (Figure 16). Second, most existing techniques heavily rely on expert user supervision for scene understanding.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Data-driven shape analysis and processing

Kim

Huang

et al. 2016

SIGGRAPH ASIA 2016 Courses

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“…Much research in vision and graphics has been devoted to recognizing, classifying, and retrieving indoor scenes, e.g., [Rasiwasia and Vasconcelos 2008;Quattoni and Torralba 2009;Fisher et al 2011;Juneja et al 2013;Xu et al 2013;Zhao et al 2014], among others. Our work recognizes the difficulty in comparing complex scenes globally, e.g., via the classic graph kernels [Fisher et al 2011].…”

Section: Scene Analysismentioning

confidence: 99%

Organizing heterogeneous scene collections through contextual focal points

Zhang

et al. 2014

ACM Trans. Graph.

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We introduce focal points for characterizing, comparing, and organizing collections of complex and heterogeneous data and apply the concepts and algorithms developed to collections of 3D indoor scenes. We represent each scene by a graph of its constituent objects and define focal points as representative substructures in a scene collection. To organize a heterogeneous scene collection, we cluster the scenes based on a set of extracted focal points: scenes in a cluster are closely connected when viewed from the perspective of the representative focal points of that cluster. The key concept of representativity requires that the focal points occur frequently in the cluster and that they result in a compact cluster. Hence, the problem of focal point extraction is intermixed with the problem of clustering groups of scenes based on their representative focal points. We present a co-analysis algorithm which interleaves frequent pattern mining and subspace clustering to extract a set of contextual focal points which guide the clustering of the scene collection. We demonstrate advantages of focal-centric scene comparison and organization over existing approaches, particularly in dealing with hybrid scenes, scenes consisting of elements which suggest membership in different semantic categories.

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Indexing 3D Scenes Using the Interaction Bisector Surface

Cited by 45 publications

References 43 publications

Building hierarchical structures for 3D scenes based on normalized cut

Building hierarchical structures for 3D scenes based on normalized cut

Data-driven shape analysis and processing

Organizing heterogeneous scene collections through contextual focal points

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