3D Retrieval System Research Articles

Improving 3D similarity search by enhancing and combining 3D descriptors

Effective content-based retrieval in 3D model databases is an important problem that has attracted much research attention over the last years. Many individual methods proposed to date rely on calculating global 3D model descriptors based on image, surface, volumetric, or structural model properties. Descriptors such as these are then input for determining the degree of similarity between models. Traditionally, the ability of individual descriptors to perform effective 3D search is decided by benchmarking. However, in practice the data set on which 3D retrieval is to be applied may differ from the characteristics of the respective benchmark. Therefore, statically determining the descriptor to use based on a fixed benchmark may lead to suboptimal results. We propose a generic strategy to improve the retrieval effectiveness in 3D retrieval systems consisting of multiple model descriptors. The specific contribution of this paper is two-fold. First, we propose to adaptively combine multiple descriptors by forming weighted descriptor combinations, where the weight of each descriptor is decided at query time. Second, we enhance the set of global model descriptors to be combined by including partial descriptors of the same kind in the combinations. Partial descriptors are obtained by applying a given descriptor extractor on the set of parts of a model, obtained by a simple model partitioning scheme. Thereby, more model information is exposed to the 3D descriptors, leading to a more complete object description. We give a systematic discussion of the descriptor combination space involving static and query-adaptive weighting schemes, and based on descriptors of different type and focus (model global vs. partial). The combination of both global and partial model descriptors is shown to deliver improved retrieval precision, compared to policies using single descriptors or fixed-weight combinations. The resulting scheme is generic and can accommodate a large class of global 3D model descriptors.

A new shape descriptor using sliced image histogram for 3D model retrieval

3-dimensional (3D) model retrieval is one of emerging research fields to find the matching shape of a given query from 3D database. This paper proposes a new shape descriptor for 3D model retrieval. A shape descriptor of 3D model requires a model normalization in order to be invariant to translation, rotation, and scale for its model. Our method is based on principal component analysis (PCA) for normalizing all the models. The shape descriptor is using a histogram of 2D images sliced along the x-, y-, and z-coordinates for measuring the similarity in 3D models. Sliced shapes for a 3D model involve a hundred planes to orthogonalize with the x-y-, and z-coordinates, respectively. Therefore, sliced shape is the 2D plane images created by intersecting at between 3D model and the planes. Our approach is to compute the slices of 3D model for the x-y-, and z-coordinates, respectively and to set by the principal axis based on principal component analysis (PCA) in order to match the 3D model between the given query and the database. Experimental results show that the proposed approach outperforms the previous approaches. We demonstrate the proposed 3D retrieval system with an intermediate example at each step.

Content-Based 3D Object Retrieval

Methods for automatically extracting descriptors from 3D objects are key to searching and indexing techniques in their growing repositories. The authors present two recently proposed approaches and discuss methods for benchmarking the 3D retrieval systems' qualitative performance.