Content-preserving image stitching with piecewise rectangular boundary constraints

This paper proposes an approach to content-preserving image stitching with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with a piecewise rectangular boundary. Existing methods treat image stitching and rectangling as two separate steps, which may result in suboptimal results as the stitching process is not aware of the further warping needs for rectangling. We address these limitations by formulating image stitching with regular boundaries in a unified optimization. Starting from the initial stitching results produced by the traditional warping-based optimization, we obtain the irregular boundary from the warped meshes by polygon Boolean operations which robustly handle arbitrary mesh compositions. By analyzing the irregular boundary, we construct a piecewise rectangular boundary. Based on this, we further incorporate line and regular boundary preservation constraints into the image stitching framework, and conduct iterative optimization to obtain an optimal piecewise rectangular boundary. Thus we can make the boundary of the stitching results as close as possible to a rectangle, while reducing unwanted distortions. We further extend our method to video stitching, by integrating the temporal coherence into the optimization. Experiments show that our method efficiently produces visually pleasing panoramas with regular boundaries and unnoticeable distortions

Stereoscopic image stitching with rectangular boundaries

This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience

Learning Thin-Plate Spline Motion and Seamless Composition for Parallax-Tolerant Unsupervised Deep Image Stitching

Traditional image stitching approaches tend to leverage increasingly complex geometric features (point, line, edge, etc.) for better performance. However, these hand-crafted features are only suitable for specific natural scenes with adequate geometric structures. In contrast, deep stitching schemes overcome the adverse conditions by adaptively learning robust semantic features, but they cannot handle large-parallax cases due to homography-based registration. To solve these issues, we propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. It provides accurate alignment for overlapping regions and shape preservation for non-overlapping regions by joint optimization concerning alignment and distortion. Subsequently, to improve the generalization capability, we design a simple but effective iterative strategy to enhance the warp adaption in cross-dataset and cross-resolution applications. Finally, to further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks. Compared with existing methods, our solution is parallax-tolerant and free from laborious designs of complicated geometric features for specific scenes. Extensive experiments show our superiority over the SoTA methods, both quantitatively and qualitatively. The code will be available at https://github.com/nie-lang/UDIS2

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

A robust patch-based synthesis framework for combining inconsistent images

Current methods for combining different images produce visible artifacts when the sources have very different textures and structures, come from far view points, or capture dynamic scenes with motions. In this thesis, we propose a patch-based synthesis algorithm to plausibly combine different images that have color, texture, structural, and geometric inconsistencies. For some applications such as cloning and stitching where a gradual blend is required, we present a new method for synthesizing a transition region between two source images, such that inconsistent properties change gradually from one source to the other. We call this process image melding. For gradual blending, we generalized patch-based optimization foundation with three key generalizations: First, we enrich the patch search space with additional geometric and photometric transformations. Second, we integrate image gradients into the patch representation and replace the usual color averaging with a screened Poisson equation solver. Third, we propose a new energy based on mixed L2/L0 norms for colors and gradients that produces a gradual transition between sources without sacrificing texture sharpness. Together, all three generalizations enable patch-based solutions to a broad class of image melding problems involving inconsistent sources: object cloning, stitching challenging panoramas, hole filling from multiple photos, and image harmonization. We also demonstrate another application which requires us to address inconsistencies across the images: high dynamic range (HDR) reconstruction using sequential exposures. In this application, the results will suffer from objectionable artifacts for dynamic scenes if the inconsistencies caused by significant scene motions are not handled properly. In this thesis, we propose a new approach to HDR reconstruction that uses information in all exposures while being more robust to motion than previous techniques. Our algorithm is based on a novel patch-based energy-minimization formulation that integrates alignment and reconstruction in a joint optimization through an equation we call the HDR image synthesis equation. This allows us to produce an HDR result that is aligned to one of the exposures yet contains information from all of them. These two applications (image melding and high dynamic range reconstruction) show that patch based methods like the one proposed in this dissertation can address inconsistent images and could open the door to many new image editing applications in the future

Invisible Seams

International audienceSurface materials are commonly described by attributes stored in textures (for instance, color, normal, or displacement). Interpolation during texture lookup provides a continuous value field everywhere on the surface, except at the chart boundaries where visible discontinuities appear. We propose a solution to make these seams invisible, while still outputting a standard texture atlas. Our method relies on recent advances in quad remeshing using global parameterization to produce a set of texture coordinates aligning texel grids across chart boundaries. This property makes it possible to ensure that the interpolated value fields on both sides of a chart boundary precisely match, making all seams invisible. However, this requirement on the uv coordinates needs to be complemented by a set of constraints on the colors stored in the texels. We propose an algorithm solving for all the necessary constraints between texel values, including through different magnification modes (nearest, bilinear, biquadratic and bicubic), and across facets using different texture resolutions. In the typical case of bilinear magnification and uniform resolution, none of the texels appearing on the surface are constrained. Our approach also ensures perfect continuity across several MIP-mapping levels