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3D movies/videos have become increasingly popular in the market; however, they are usually produced by professionals. This paper presents a new technique for the automatic conversion of 2D to 3D video based on RGB-D sensors, which can be easily conducted by ordinary users. To generate a 3D image, one approach is to combine the original 2D color image and its corresponding depth map together to perform depth image-based rendering (DIBR). An RGB-D sensor is one of the inexpensive ways to capture an image and its corresponding depth map. The quality of the depth map and the DIBR algorithm are crucial to this process. Our approach is twofold. First, the depth maps captured directly by RGB-D sensors are generally of poor quality because there are many regions missing depth information, especially near the edges of objects. This paper proposes a new RGB-D sensor based depth map inpainting method that divides the regions with missing depths into interior holes and border holes. Different schemes are used to inpaint the different types of holes. Second, an improved hole filling approach for DIBR is proposed to synthesize the 3D images by using the corresponding color images and the inpainted depth maps. Extensive experiments were conducted on different evaluation datasets. The results show the effectiveness of our method.
相似文献Over the last few years, there has been a growing interest in augmented reality (AR) technology for education. However, current AR education applications are often used as a new type of knowledge display platform, and they cannot fully participate in educational activities to improve educational results. To enable AR technology to participate in educational activities more effectively, according to learning-by-doing theory, we explore the form of a future experimental course and propose a new AR-based multimedia environment for experimental education. The framework of the multimedia environment consists of three components: the AR experiment authoring tool, the AR experiment application, and the management application. In this AR-based multimedia environment, teachers can independently create AR experiments using the what you see is what you get (WYSIWYG) editing method. Students can manipulate the AR-based experimental object to complete the experiment in class. Moreover, teachers can observe students’ experimental behaviour, obtain evaluations in real time, and even guide students remotely. We also present an application case of a chemistry experiment and obtain results of the usability test, demonstrating improvements in AR technology participation in educational activities.
相似文献Person re-identification (re-id) aims to identity the same person over multiple cameras; it has been successfully applied to various computer vision applications as a fundamental method. Owing to the development of deep learning, person re-id methods, which typically use triplet networks based on triplet loss, have demonstrated great success. However, the appearances of people are similar and hence difficult to distinguish in many cases. Therefore, we present a novel graph convolution network and enhances traditional triplet loss functions. Our method defines reference, positive, and negative features for triplet loss as three vertices of a graph, respectively, and adjusts their mutual distance through learning. The method adopts graph convolutions efficiently, thereby affording low computational costs. Experimental results demonstrate that our method is superior to the baseline on the Market-1501 dataset. The proposed GCN-based triplet loss considerably contributes to improve re-identification methods quantitatively and qualitatively.
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