George Leifman

Mesh segmentation using feature point and core extraction

Mesh segmentation has become a necessary ingredient in many applications in computer graphics. This paper proposes a novel hierarchical mesh segmentation algorithm, which is based on new methods for prominent feature point and core extraction. The algorithm has several benefits. First, it is invariant both to the pose of the model and to different proportions between the model’s components. Second, it produces correct hierarchical segmentations of meshes, both in the coarse levels of the hierarchy and in the fine levels, where tiny segments are extracted. Finally, the boundaries between the segments go along the natural seams of the models.

Semantic-oriented 3d shape retrieval using relevance feedback ∗

Shape-based retrieval of 3D models has become an important challenge in computer graphics. Object similarity, however, is a subjective matter, dependent on the human viewer, since objects have semantics and are not mere geometric entities. Relevance feedback aims at addressing the subjectivity of similarity. This paper presents a novel relevance feedback algorithm that is based on supervised as well as unsupervised feature extraction techniques. It also proposes a novel signature for 3D models, the sphere projection. A Web search engine that realizes the signature and the relevance feedback algorithm is presented. We show that the proposed approach produces good results and outperforms previous techniques.

Paper craft models from meshes

This paper introduces an algorithm for segmenting a mesh into developable approximations. The algorithm can be used in various applications in CAD and computer graphics. This paper focuses on paper crafting and demonstrates that the algorithm generates approximations that are developable, easy to cut, and can be glued together. It is also shown that the error between the given model and the paper model is small.

Surface regions of interest for viewpoint selection

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. It is also shown how this algorithm can be adopted to saliency detection in point clouds. Many applications can utilize these regions. In this paper we explore one such application-viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.

Saliency Detection in Large Point Sets

While saliency in images has been extensively studied in recent years, there is very little work on saliency of point sets. This is despite the fact that point sets and range data are becoming ever more widespread and have myriad applications. In this paper we present an algorithm for detecting the salient points in unorganized 3D point sets. Our algorithm is designed to cope with extremely large sets, which may contain tens of millions of points. Such data is typical of urban scenes, which have recently become commonly available on the web. No previous work has handled such data. For general data sets, we show that our results are competitive with those of saliency detection of surfaces, although we do not have any connectivity information. We demonstrate the utility of our algorithm in two applications: producing a set of the most informative viewpoints and suggesting an informative city tour given a city scan.

Minimal-cut model composition

Constructing new, complex models is often done by reusing parts of existing models, typically by applying a sequence of segmentation, alignment and composition operations. Segmentation, either manual or automatic, is rarely adequate for this task, since it is applied to each model independently, leaving it to the user to trim the models and determine where to connect them. In this paper we propose a new composition tool. Our tool obtains as input two models, aligned either manually or automatically, and a small set of constraints indicating which portions of the two models should be preserved in the final output. It then automatically negotiates the best location to connect the models, trimming and stitching them as required to produce a seamless result. We offer a method based on the graph theoretic minimal cut as a means of implementing this new tool. We describe a system intended for both expert and novice users, allowing easy and flexible control over the composition result. In addition, we show our method to be well suited for a variety of model processing applications such as model repair, hole filling, and piecewise rigid deformations.

Mesh Colorization

This paper proposes a novel algorithm for colorization of meshes. This is important for applications in which the model needs to be colored by just a handful of colors or when no relevant image exists for texturing the model. For instance, archaeologists argue that the great Roman or Greek statues were full of color in the days of their creation, and traces of the original colors can be found. In this case, our system lets the user scribble some desired colors in various regions of the mesh. Colorization is then formulated as a constrained quadratic optimization problem, which can be readily solved. Special care is taken to avoid color bleeding between regions, through the definition of a new direction field on meshes.

Reconstruction of relief objects from line drawings

This paper addresses the problem of automatic reconstruction of a 3D relief from a line drawing on top of a given base object. Reconstruction is challenging due to four reasons–the sparsity of the strokes, their ambiguity, their large number, and their inter-relations. Our approach is able to reconstruct a model from a complex drawing that consists of many inter-related strokes. Rather than viewing the inter-dependencies as a problem, we show how they can be exploited to automatically generate a good initial interpretation of the line drawing. Then, given a base and an interpretation, we propose an algorithm for reconstructing a consistent surface. The strength of our approach is demonstrated in the reconstruction of archaeological artifacts from drawings. These drawings are highly challenging, since artists created very complex and detailed descriptions of artifacts regardless of any considerations concerning their future use for shape reconstruction.

Surface Regions of Interest for Viewpoint Selection

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. Many applications can utilize these regions. In this paper we explore one such application - viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.

Pattern-Driven Colorization of 3D Surfaces

Colorization refers to the process of adding color to black and white images or videos. This paper extends the term to handle surfaces in three dimensions. This is important for applications in which the colors of an object need to be restored and no relevant image exists for texturing it. We focus on surfaces with patterns and propose a novel algorithm for adding colors to these surfaces. The user needs only to scribble a few color strokes on one instance of each pattern, and the system proceeds to automatically colorize the whole surface. For this scheme to work, we address not only the problem of colorization, but also the problem of pattern detection on surfaces.