The distillation can also cover 3D object boxes on the background.

This supplementary content is mainly organized in the order of being referenced in the main manuscript. The architectures of the R networks are shown in Table 3. The training curve is shown in Figure 1. B.1 Where is the secret image encoded? Is every channel equally important?

Color image generation has a wide range of applications, but the existing generation models ignore the correlation among color channels, which may lead to chromatic aberration problems. In addition, the data distribution problem of color images has not been systematically elaborated and explained, so that there is still the lack of the theory about measuring different color images datasets. In this paper, we define a new quaternion Wasserstein distance and develop its dual theory. To deal with the quaternion linear programming problem, we derive the strong duality form with helps of quaternion convex set separation theorem and quaternion Farkas lemma. With using quaternion Wasserstein distance, we propose a novel Wasserstein quaternion generative adversarial network. Experiments demonstrate that this novel model surpasses both the (quaternion) generative adversarial networks and the Wasserstein generative adversarial network in terms of generation efficiency and image quality.

Input features are conventionally represented as vectors, matrices, or third order tensors in the real field, for color image classification. Inspired by the success of quaternion data modeling for color images in image recovery and denoising tasks, we propose a novel classification method for color image classification, named as the Low-rank Support Quaternion Matrix Machine (LSQMM), in which the RGB channels are treated as pure quaternions to effectively preserve the intrinsic coupling relationships among channels via the quaternion algebra. For the purpose of promoting low-rank structures resulting from strongly correlated color channels, a quaternion nuclear norm regularization term, serving as a natural extension of the conventional matrix nuclear norm to the quaternion domain, is added to the hinge loss in our LSQMM model. An Alternating Direction Method of Multipliers (ADMM)-based iterative algorithm is designed to effectively resolve the proposed quaternion optimization model. Experimental results on multiple color image classification datasets demonstrate that our proposed classification approach exhibits advantages in classification accuracy, robustness and computational efficiency, compared to several state-of-the-art methods using support vector machines, support matrix machines, and support tensor machines.

We learn the camera sensor's color multiplexing pattern by encoding it as layer whose learnable weights determine which color

Abstract--In this work, we introduce Wonder3D++, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. T o holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. T o ensure the consistency of generation, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a cascaded 3D mesh extraction algorithm that drives high-quality surfaces from the multi-view 2D representations in only about 3 minute in a coarse-to-fine manner . Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works. Reconstructing 3D geometry from a single image [13], [25], [36], [42], [43], [46], [49] stands as a fundamental task in computer graphics and 3D computer vision, benefiting a wide range of versatile applications such as novel view synthesis [7], [24], [39], 3D content creation [38], [53], and robotics grasping [29], [93]. However, this task is notably challenging since it is ill-posed and demands the ability to discern the 3D geometry of both visible and invisible parts. This ability requires extensive knowledge of the 3D world. Recently, the field of 3D generation has experienced rapid and flourishing development with the introduction of diffusion models. Y uxiao Y ang and Xiao-Xiao Long contribute equally to this work. Y uxiao Y ang is with Tsinghua University and also with Nanjing University. Xiaoxiao Long is with Nanjing University. Zhiqiang Wu and Wei Yin are the corresponding authors.

To address these challenges, we propose boosting IR's performance by sharing the

The distillation can also cover 3D object boxes on the background.

This supplementary content is mainly organized in the order of being referenced in the main manuscript. The architectures of the R networks are shown in Table 3. The training curve is shown in Figure 1. B.1 Where is the secret image encoded? Is every channel equally important?