Daniele Panozzo

NewsStand: a new view on news

News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of todays news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStands map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.

Locally injective mappings

Mappings and deformations are ubiquitous in geometry processing, shape modeling, and animation. Numerous deformation energies have been proposed to tackle problems like mesh parameterization and volumetric deformations. We present an algorithm that modifies any deformation energy to guarantee a locally injective mapping, i.e., without inverted elements. Our formulation can be used to compute continuous planar or volumetric piecewise‐linear maps and it uses a barrier term to prevent inverted elements. Differently from previous methods, we carefully design both the barrier term and the associated numerical techniques to be able to provide immediate feedback to the user, enabling interactive manipulation of inversion‐free mappings. Stress tests show that our method robustly handles extreme deformations where previous techniques converge very slowly or even fail. We demonstrate that enforcing local injectivity increases fidelity of the results in applications such as shape deformation and parameterization.

Robust Image Retargeting via Axis-Aligned Deformation

We propose the space of axis‐aligned deformations as the meaningful space for content‐aware image retargeting. Such deformations exclude local rotations, avoiding harmful visual distortions, and they are parameterized in 1D. We show that standard warping energies for image retargeting can be minimized in the space of axis‐aligned deformations while guaranteeing that bijectivity constraints are satisfied, leading to high‐quality, smooth and robust retargeting results. Thanks to the 1D parameterization, our method only requires solving a small quadratic program, which can be done within a few milliseconds on the CPU with no precomputation overhead. We demonstrate how the image size and the saliency map can be changed in real time with our approach, and present results on various input images, including the RetargetMe benchmark. We compare our results with six other algorithms in a user study to demonstrate that the space of axis‐aligned deformations is suitable for the problem at hand.

Designing unreinforced masonry models

We present a complete design pipeline that allows non-expert users to design and analyze masonry structures without any structural knowledge. We optimize the force layouts both geometrically and topologically, finding a self-supported structure that is as close as possible to a given target surface. The generated structures are tessellated into hexagonal blocks with a pattern that prevents sliding failure. The models can be used in physically plausible virtual environments or 3D printed and assembled without reinforcements.

Practical quad mesh simplification

In this paper we present an innovative approach to incremental quad mesh simplification, i.e. the task of producing a low complexity quad mesh starting from a high complexity one. The process is based on a novel set of strictly local operations which preserve quad structure. We show how good tessellation quality (e.g. in terms of vertex valencies) can be achieved by pursuing uniform length and canonical proportions of edges and diagonals. The decimation process is interleaved with smoothing in tangent space. The latter strongly contributes to identify a suitable sequence of local modification operations. The method is naturally extended to manage preservation of feature lines (e.g. creases) and varying (e.g. adaptive) tessellation densities. We also present an original Triangle‐to‐Quad conversion algorithm that behaves well in terms of geometrical complexity and tessellation quality, which we use to obtain the initial quad mesh from a given triangle mesh.

Simple quad domains for field aligned mesh parametrization

We present a method for the global parametrization of meshes that preserves alignment to a cross field in input while obtaining a parametric domain made of few coarse axis-aligned rectangular patches, which form an abstract base complex without T-junctions. The method is based on the topological simplification of the cross field in input, followed by global smoothing.

Reading news with maps by exploiting spatial synonyms

Use this map query interface to search the world, even when not sure what information you seek.

Instant field-aligned meshes

We present a novel approach to remesh a surface into an isotropic triangular or quad-dominant mesh using a unified local smoothing operator that optimizes both the edge orientations and vertex positions in the output mesh. Our algorithm produces meshes with high isotropy while naturally aligning and snapping edges to sharp features. The method is simple to implement and parallelize, and it can process a variety of input surface representations, such as point clouds, range scans and triangle meshes. Our full pipeline executes instantly (less than a second) on meshes with hundreds of thousands of faces, enabling new types of interactive workflows. Since our algorithm avoids any global optimization, and its key steps scale linearly with input size, we are able to process extremely large meshes and point clouds, with sizes exceeding several hundred million elements. To demonstrate the robustness and effectiveness of our method, we apply it to hundreds of models of varying complexity and provide our cross-platform reference implementation in the supplemental material.

Object detection and classification from large-scale cluttered indoor scans

We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly‐planar patches related by geometric transformations. This representation enables us to robustly filter out noise and greatly reduces the computational cost and memory requirements of our method, enabling execution at interactive rates. We propose a patch similarity measure based on shape descriptors and spatial configurations of neighboring patches. The patches are clustered in a Euclidean embedding space based on the similarity matrix to yield the segmentation of the input point cloud. The generated segmentation can be used to compress the raw point cloud, create an object database, and increase the clarity of the point cloud visualization.

Designing N -PolyVector fields with complex polynomials

We introduce N‐PolyVector fields, a generalization of N‐RoSy fields for which the vectors are neither necessarily orthogonal nor rotationally symmetric. We formally define a novel representation for N‐PolyVectors as the root sets of complex polynomials and analyze their topological and geometric properties. A smooth N‐PolyVector field can be efficiently generated by solving a sparse linear system without integer variables. We exploit the flexibility of N‐PolyVector fields to design conjugate vector fields, offering an intuitive tool to generate planar quadrilateral meshes.