Philip Shilane

The Princeton Shape Benchmark

In recent years, many shape representations and geometric algorithms have been proposed for matching 3D shapes. Usually, each algorithm is tested on a different (small) database of 3D models, and thus no direct comparison is available for competing methods. We describe the Princeton Shape Benchmark (PSB), a publicly available database of polygonal models collected from the World Wide Web and a suite of tools for comparing shape matching and classification algorithms. One feature of the benchmark is that it provides multiple semantic labels for each 3D model. For instance, it includes one classification of the 3D models based on function, another that considers function and form, and others based on how the object was constructed (e.g., man-made versus natural objects). We find that experiments with these classifications can expose different properties of shape-based retrieval algorithms. For example, out of 12 shape descriptors tested, extended Gaussian images by B. Horn (1984) performed best for distinguishing man-made from natural objects, while they performed among the worst for distinguishing specific object types. Based on experiments with several different shape descriptors, we conclude that no single descriptor is best for all classifications, and thus the main contribution of this paper is to provide a framework to determine the conditions under which each descriptor performs best.

Modeling by example

In this paper, we investigate a data-driven synthesis approach to constructing 3D geometric surface models. We provide methods with which a user can search a large database of 3D meshes to find parts of interest, cut the desired parts out of the meshes with intelligent scissoring, and composite them together in different ways to form new objects. The main benefit of this approach is that it is both easy to learn and able to produce highly detailed geometric models -- the conceptual design for new models comes from the user, while the geometric details come from examples in the database. The focus of the paper is on the main research issues motivated by the proposed approach: (1) interactive segmentation of 3D surfaces, (2) shape-based search to find 3D models with parts matching a query, and (3) composition of parts to form new models. We provide new research contributions on all three topics and incorporate them into a prototype modeling system. Experience with our prototype system indicates that it allows untrained users to create interesting and detailed 3D models.

A planar-reflective symmetry transform for 3D shapes

Symmetry is an important cue for many applications, including object alignment, recognition, and segmentation. In this paper, we describe a planar reflective symmetry transform (PRST) that captures a continuous measure of the reflectional symmetry of a shape with respect to all possible planes. This transform combines and extends previous work that has focused on global symmetries with respect to the center of mass in 3D meshes and local symmetries with respect to points in 2D images. We provide an efficient Monte Carlo sampling algorithm for computing the transform for surfaces and show that it is stable under common transformations. We also provide an iterative refinement algorithm to find local maxima of the transform precisely. We use the transform to define two new geometric properties, center of symmetry and principal symmetry axes, and show that they are useful for aligning objects in a canonical coordinate system. Finally, we demonstrate that the symmetry transform is useful for several applications in computer graphics, including shape matching, segmentation of meshes into parts, and automatic viewpoint selection.

Partial matching of 3D shapes with priority-driven search

Priority-driven search is an algorithm for retrieving similar shapes from a large database of 3D objects. Given a query object and a database of target objects, all represented by sets of local 3D shape features, the algorithm produces a ranked list of the c best target objects sorted by how well any subset of k features on the query match features on the target object. To achieve this goal, the system maintains a priority queue of potential sets of feature correspondences (partial matches) sorted by a cost function accounting for both feature dissimilarity and the geometric deformation. Only partial matches that can possibly lead to the best full match are popped off the queue, and thus the system is able to find a provably optimal match while investigating only a small subset of potential matches. New methods based on feature distinction, feature correspondences at multiple scales, and feature difference ranking further improve search time and retrieval performance. In experiments with the Princeton Shape Benchmark, the algorithm provides significantly better classification rates than previously tested shape matching methods while returning the best matches in a few seconds per query.

Distinctive regions of 3D surfaces

Selecting the most important regions of a surface is useful for shape matching and a variety of applications in computer graphics and geometric modeling. While previous research has analyzed geometric properties of meshes in isolation, we select regions that distinguish a shape from objects of a different type. Our approach to analyzing distinctive regions is based on performing a shape-based search using each region as a query into a database. Distinctive regions of a surface have shape consistent with objects of the same type and different from objects of other types. We demonstrate the utility of detecting distinctive surface regions for shape matching and other graphics applications including mesh visualization, icon generation, and mesh simplification.

Shape-based retrieval and analysis of 3d models

The number of 3D geometric models available in online repositories is growing dramatically. Examples include: the Protein Data Bank [1], which stores the 3D atomic coordinates for 29,000 protein molecules; the National Design Repository [9], which stores 3D computer-aided design (CAD) models for tens of thousands of mechanical parts; and the Princeton Shape Database [5], which stores polygonal surface models for 36,000 everyday objects crawled from the Web. Since graphics hardware is getting faster and 3D scanning hardware cheaper, there is every reason to believe that demand for and supply of 3D models will continue to increase into the future, leading to an online environment in which 3D models are as plentiful as images, videos, and audio files today.

Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

Databases of 3D shapes have become widespread for a variety of applications, and a key research problem is searching these databases for similar shapes. This paper introduces a method for finding distinctive features of a shape that are useful for determining shape similarity. Although global shape descriptors have been developed to facilitate retrieval, they fail when local shape properties are the distinctive features of a class. Alternatively, local shape descriptors can be generated over the surface of shapes, but then storage and search of the descriptors becomes unnecessarily expensive, as perhaps only a few descriptors are sufficient to distinguish classes. The challenge is to select local descriptors from a query shape that are most distinctive for retrieval. Our approach is to define distinction as the retrieval performance of a local shape descriptor. During a training phase, we estimate descriptor likelihood using a multi-variate Gaussian distribution of real-valued shape descriptors, evaluate the retrieval performance of each descriptor from a training set, and average these performance values at every likelihood value. For each query, we evaluate the likelihood of local shape descriptors on its surface and lookup the expected retrieval values learned from the training set to determine their predicted distinction values. We show that querying with the most distinctive shape descriptors provides favorable retrieval performance during tests with a database of common graphics objects

WAN-optimized replication of backup datasets using stream-informed delta compression

Replicating data off site is critical for disaster recovery reasons, but the current approach of transferring tapes is cumbersome and error prone. Replicating across a wide area network (WAN) is a promising alternative, but fast network connections are expensive or impractical in many remote locations, so improved compression is needed to make WAN replication truly practical. We present a new technique for replicating backup datasets across a WAN that not only eliminates duplicate regions of files (deduplication) but also compresses similar regions of files with delta compression, which is available as a feature of EMC Data Domain systems. Our main contribution is an architecture that adds stream-informed delta compression to already existing deduplication systems and eliminates the need for new, persistent indexes. Unlike techniques based on knowing a files version or that use a memory cache, our approach achieves delta compression across all data replicated to a server at any time in the past. From a detailed analysis of datasets and statistics from hundreds of customers using our product, we achieve an additional 2X compression from delta compression beyond deduplication and local compression, which enables customers to replicate data that would otherwise fail to complete within their backup window.

Stratified point sampling of 3D models

Point sampling is an important intermediate step for a variety of computer graphics applications, and specialized sampling strategies have been developed to satisfy the requirements of each problem. In this article, we present a technique to generate a stratified sampling of 3D models that is applicable across many domains. The algorithm voxelizes the model and selects one sample per voxel, restricted to the original models surface. Parameters allow control of the uniformity of the sample placement and the minimum distance between samples. We demonstrate the effectiveness of this technique in selecting stroke locations for painterly rendering models and for producing sampled geometry used as input to shape descriptors.

A Comprehensive Study of the Past, Present, and Future of Data Deduplication

Data deduplication, an efficient approach to data reduction, has gained increasing attention and popularity in large-scale storage systems due to the explosive growth of digital data. It eliminates redundant data at the file or subfile level and identifies duplicate content by its cryptographically secure hash signature (i.e., collision-resistant fingerprint), which is shown to be much more computationally efficient than the traditional compression approaches in large-scale storage systems. In this paper, we first review the background and key features of data deduplication, then summarize and classify the state-of-the-art research in data deduplication according to the key workflow of the data deduplication process. The summary and taxonomy of the state of the art on deduplication help identify and understand the most important design considerations for data deduplication systems. In addition, we discuss the main applications and industry trend of data deduplication, and provide a list of the publicly available sources for deduplication research and studies. Finally, we outline the open problems and future research directions facing deduplication-based storage systems.