Portraits near the periphery of wide-angle photos often suffer conspicuous distortions. With the popularity of wide-angle lenses on mobile phones, portrait correction, which removes portrait distortion in wide-angle photos, has attracted widespread attention as a form of content-aware warping. Existing portrait correction methods for wide-angle photos using uniform quad meshes take a long time to optimize the correction. Most of them focus only on correcting facial distortions, leading to inconsistency between people's heads and bodies after correction. This study proposes an efficient method to remove portrait distortions in wide-angle perspective photos, based on a triangle mesh. We generate an adaptive mesh tailored to the image content with relatively few vertices. According to the characteristics of the triangle mesh, we tailor three smooth and intuitive energy terms for the human area, background area, and boundary to minimize portrait distortions. Our algorithm can easily be extended to allow further geometric constraints, such as line constraints. Experimental results show that our method is robust for photos with various fields of view. Comparisons to the state-of-the-art demonstrate that our method achieves significant improvements in optimization efficiency and consistency of heads and bodies.

In this paper, we propose a method that can extract enhanced color palettes for images, which are characterized by a heightened level of representativeness for images. The resulted palettes are not only easy to compute but also effectively convey the distribution of pixels across the color space. Based on these extracted palettes, we present a tailored color palette matching algorithm designed for our image color transfer method by solving an optimization problem to transfer colors from a reference image to the original image. Our algorithm offers the flexibility to operate in fully automatic mode or provide various levels of user interactivity, allowing for coarse-to-fine editing. Moreover, we demonstrate the adaptability of our palette-based color transfer method to diverse applications, including grayscale image colorization and temporally consistent, time-varying video color transfer. Extensive experiments and comparisons have been conducted to measure the quality of our results, employing both visual assessments and evaluation metrics. These findings demonstrate that our palette-based color transfer method efficiently and faithfully transfers color styles from reference images to both color and grayscale images and videos.

Feature-preserving mesh reconstruction from point clouds is challenging. Implicit methods tend to fit smooth surfaces and cannot be used to reconstruct sharp features. Explicit reconstruction methods are sensitive to noise and only interpolate sharp features when points are distributed on feature lines. We propose a watertight surface reconstruction method based on optimal transport that can accurately reconstruct sharp features often present in CAD models. We formalize the surface reconstruction problem by minimizing the optimal transport cost between the point cloud and the reconstructed surface. The algorithm consists of initialization and refinement steps. In the initialization step, the convex hull of the point cloud is deformed under the guidance of a transport plan to obtain an initial approximate surface. Next, the mesh surface was optimized using operations including vertex relocation and edge collapses/flips to obtain feature-preserving results. Experiments demonstrate that our method can preserve sharp features while being robust to noise and missing data.

Poisson disk sampling is an important problem in computer graphics and has a wide variety of applications in imaging, geometry, rendering, etc. In this paper, we propose a novel Poisson disk sampling algorithm based on disk packing. The key idea uses the observation that a relatively dense disk packing layout naturally satisfies the Poisson disk distribution property that each point is no closer to the others than a specified minimum distance, i.e., the Poisson disk radius. We use this property to propose a relaxation algorithm that achieves a good balance between the random and uniform properties needed for Poisson disk distributions. Our algorithm is easily adapted to image stippling by extending identical disk packing to unequal disks. Experimental results demonstrate the efficacy of our approaches.