Camera Constraint-Free View-Based 3-D Object Retrieval

Abstract

Recently, extensive research efforts have been dedicated to view-based methods for 3-D object retrieval due to the highly discriminative property of multiviews for 3-D object representation. However, most of state-of-the-art approaches highly depend on their own camera array settings for capturing views of 3-D objects. In order to move toward a general framework for 3-D object retrieval without the limitation of camera array restriction, a camera constraint-free view-based (CCFV) 3-D object retrieval algorithm is proposed in this paper. In this framework, each object is represented by a free set of views, which means that these views can be captured from any direction without camera constraint. For each query object, we first cluster all query views to generate the view clusters, which are then used to build the query models. For a more accurate 3-D object comparison, a positive matching model and a negative matching model are individually trained using positive and negative matched samples, respectively. The CCFV model is generated on the basis of the query Gaussian models by combining the positive matching model and the negative matching model. The CCFV removes the constraint of static camera array settings for view capturing and can be applied to any view-based 3-D object database. We conduct experiments on the National Taiwan University 3-D model database and the ETH 3-D object database. Experimental results show that the proposed scheme can achieve better performance than state-of-the-art methods.

Efficient Salient Region Detection with Soft Image Abstraction

“…This kind of decomposition is a key component of many computer vision and graphics tasks. Rather than focusing on predicting human fixation points [6,32] (another major research direction of visual attention modeling), salient region detection methods aim at uniformly highlighting entire salient object regions, thus benefiting a large number of applications, including objectof-interest image segmentation [19], adaptive compression [17], object recognition [44], content aware image editing [51], object level image manipulation [12,15,53], and internet visual media retrieval [10,11,13,29,24,23].…”

Real-time selection of video streams for live TV broadcasting based on Query-by-Example using a 3D model

“…Finally, it is interesting to mention that other proposals focus on the problem of retrieving 3D object models (e.g, [31,32,33,34,35]) or videos (e.g., [36]) instead of images. To improve the efficiency and accuracy of view-based 3D object retrieval, in [35] the authors propose to select the most interesting 2D views using a probabilistic Bayesian method (Adaptive Views Clustering), whereas in [33] the authors present an algorithm that minimizes the number of query views required based on information extracted from the query and the users' relevance feedback.…”

“…In [34] the query to retrieve 3D models is a set of views, but no camera constraint must be specified (so, any view set captured by any camera array can be used as a query). In [32] the authors present an algorithm to retrieve 3D models by querying-by-sketch based on the alignment of 3D models to 2D sketches.…”

IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

“…Most previous studies focus on encoding and retrieving 3D polygon models using polygon models as input queries (Funkhouser et al, 2003;Assfalg et al, 2007;Gao et al, 2011;Akgul et al, 2009;Gao et al, 2012). However, these studies do not consider model retrieval by using point clouds, which is in a great need in the topic of efficient cyber city construction with airborne LiDAR point clouds.…”

“…For view-based retrieval, 3D shapes are represented as a set of 2D projections and 3D models are matched using their visual similarities rather than the geometric similarities (Gao et al, 2011;Gao et al, 2012;Chen et al, 2003;Stavropoulos et al, 2010;Papadakisa et al, 2007). Each projection is described by image descriptors.…”