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ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

The process of aligning a pair of shapes is a fundamental operation in computer graphics. Traditional approaches rely heavily on matching corresponding points or features to guide the alignment, a paradigm that falters when significant shape portions are missing. These techniques generally do not incorporate prior knowledge about expected shape... (more)

Surface Reconstruction Based on the Modified Gauss Formula

In this article, we introduce a surface reconstruction method that has excellent performance despite nonuniformly distributed, noisy, and sparse data.... (more)

Analytic Eigensystems for Isotropic Distortion Energies

Many strategies exist for optimizing non-linear distortion energies in geometry and physics applications, but devising an approach that achieves the... (more)

Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction

We introduce a new method that efficiently computes a set of viewpoints and trajectories for high-quality 3D reconstructions in outdoor environments. Our goal is to automatically explore an unknown area and obtain a complete 3D scan of a region of interest (e.g., a large building). Images from a commodity RGB camera, mounted on an autonomously... (more)

Interlinked SPH Pressure Solvers for Strong Fluid-Rigid Coupling

We present a strong fluid-rigid coupling for Smoothed Particle Hydrodynamics (SPH) fluids and rigid bodies with particle-sampled surfaces. The... (more)

EasyFont: A Style Learning-Based System to Easily Build Your Large-Scale Handwriting Fonts

Generating personal handwriting fonts with large amounts of characters is a boring and time-consuming task. For example, the official standard GB18030-2000 for commercial font products consists of 27,533 Chinese characters. Consistently and correctly writing out such huge amounts of characters is usually an impossible mission for ordinary people.... (more)

Steklov Spectral Geometry for Extrinsic Shape Analysis

We propose using the Dirichlet-to-Neumann operator as an extrinsic alternative to the Laplacian for spectral geometry processing and shape analysis.... (more)

Functional Characterization of Deformation Fields

In this article, we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric.... (more)

Vectorization of Line Drawings via Polyvector Fields

Image tracing is a foundational component of the workflow in graphic design, engineering, and computer animation, linking hand-drawn concept images to... (more)

Knittable Stitch Meshes

We introduce knittable stitch meshes for modeling complex 3D knit structures that can be fabricated via knitting. We extend the concept of stitch mesh modeling, which provides a powerful 3D design interface for knit structures but lacks the ability to produce actually knittable models. Knittable stitch meshes ensure that the final model can be... (more)

LineUp: Computing Chain-Based Physical Transformation

In this article, we introduce a novel method that can generate a sequence of physical transformations between 3D models with different shape and topology. Feasible transformations are realized on a chain structure with connected components that are 3D printed. Collision-free motions are computed to transform between different configurations of the... (more)

NEWS

New Submission Requirements

As of October 2018, ACM TOG requires submissions for review to be anonymous. See the Author Guidelines for details.  

About TOG

ACM TOG is the foremost peer-reviewed journal in the area of computer graphics. 

Recent impact factor calculations from Thomson Reuters give ACM TOG an impact factor of 4.096 and an Eigenfactor Score of 0.029, giving it the top ranking among the 104 journals in the Computer Science: Software Engineering category. 

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Forthcoming Articles

Poly-Spline Finite Element Method

Reversible Harmonic Maps between Discrete Surfaces

Information transfer between triangle meshes is of great importance in computer graphics and geometry processing. To facilitate this process, a smooth and accurate map is typically required between the two meshes. While such maps can sometimes be computed between nearly-isometric meshes, the more general case of meshes with diverse geometries remains challenging. We propose a novel approach for direct map computation between triangle meshes without mapping to an intermediate domain, which optimizes for the harmonicity and reversibility of the forward and backward maps. Our method is general both in the information it can receive as input, e.g. point landmarks, a dense map or a functional map, and in the diversity of the geometries to which it can be applied. We demonstrate that our maps exhibit lower conformal distortion than the state-of-the-art, while succeeding in correctly mapping key features of the input shapes.

Video Extrapolation using Neighboring Frames

With the popularity of immersive display systems that fill the viewers field of view (FOV) entirely, demand for wide FOV content has increased. A video extrapolation technique based on reuse of existing videos is one of the most efficient ways to produce wide FOV content. Extrapolating a video poses a great challenge, however, due to the insufficient amount of cues and information that can be leveraged for the estimation of the extended region. This paper introduces a novel framework that allows the extrapolation of an input video and consequently converts a conventional content into one with wide FOV. The key idea of the proposed approach is to integrate the information from all frames in the input video into each frame. Utilizing the information from all frames is crucial because it is very difficult to achieve the goal with a 2D transformation based approach when parallax caused by camera motion is apparent. Warping guided by scene-space information matches the viewpoints between the different frames. The matched frames are blended to create extended views. Various experiments demonstrate that the results of the proposed method are more visually plausible than those produced using state-of-the-art techniques.

Progressive Color Transfer with Dense Semantic Correspondences

We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semantically-meaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially-variant and globally coherent. Therefore, our algorithm optimizes a local linear model for color transfer satisfying both local and global constraints. Our proposed approach jointly optimizes matching and color transfer, adopting a coarse-to-fine strategy. The proposed method can be successfully extended from "one-to-one" to "one-to-many" color transfers. The latter further addresses the problem of mismatching elements of the input image. We validate our proposed method by testing it on a large variety of image content.

Redefining A in RGBA: Towards a Standard for Graphical 3D Printing

Advances in multimaterial 3D printing have the potential to reproduce various visual appearance attributes of an object in addition to its shape. Since many existing 3D file formats encode color and translucency by RGBA textures mapped to 3D shapes, RGBA information is particularly important for practical applications. In contrast to color (encoded by RGB), translucency (encoded by A) is neither linked to any measurable physical nor perceptual quantity and is, therefore, open for interpretation. In this paper, we propose a rigorous definition for A suitable for graphical 3D printing which links both optical material properties and perceptual uniformity for human observers. By deriving our definition from the absorption and scattering coefficients of virtual reference materials with an isotropic phase function, we achieve two important properties. First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is rescaled for printing. Second, determining A for a real material can be achieved by minimizing a distance function between light transport measurements of this material (conducted by commercial spectrophotometers) and simulated measurements of the reference materials. Finally, we derive from visual experiments an embedding of A into a nearly perceptually-uniform scale of translucency for the reference materials.

Non-line-of-sight Imaging with Partial Occluders and Surface Normals

Imaging objects obscured by occluders is a significant challenge for many applications. A camera that could ``see around corners'' could help improve navigation and mapping capabilities of autonomous vehicles or make search and rescue missions more effective. Time-resolved single-photon imaging systems have recently been demonstrated to record optical information of a scene that can lead to an estimation of the shape and reflectance of objects hidden from the line of sight of a camera. However, existing non-line-of-sight (NLOS) reconstruction algorithms have been constrained in the types of light transport effects they model for the hidden scene parts. We introduce a factored NLOS light transport representation that accounts for partial occlusions and surface normals. Based on this model, we develop a factorization approach for inverse time-resolved light transport and demonstrate high-fidelity NLOS reconstructions for challenging scenes both in simulation and with an experimental NLOS imaging system.

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