Image matching between the optical and synthetic aperture radar (SAR) is one of the most fundamental problems for earth observation. In recent years, many researchers have used hand-made descriptors with their expertise to find matches between optical and SAR images. However, due to the large nonlinear radiation difference between optical images and SAR images, the image matching becomes very difficult. To deal with the problems, the article proposes an efficient feature matching and position matching algorithm (MatchosNet) based on local deep feature descriptor. First, A new dataset is presented by collecting a large number of corresponding SAR images and optical images. Then a deep convolutional network with dense blocks and cross stage partial networks is designed to generate deep feature descriptors. Next, the hard L2 loss function and ARCpatch loss function are designed to improve matching effect. In addition, on the basis of feature matching, the two-dimensional (2-D) Gaussian function voting algorithm is designed to further match the position of optical images and SAR images of different sizes. Finally, a large number of quantitative experiments show that MatchosNet has a excellent matching effect in feature matching and position matching. The code will be released at: https://github.com/LiaoYun0x0/Feature-Matching-and-Position-Matching-between-Optical-and-SAR .

View synthesis is a fundamental task in computer vision, known for its significantly higher complexity compared to conventional vision problems. The introduction of Neural Radiance Fields (NeRF) marked a major breakthrough in this field, substantially improving previous methods and pushing view synthesis to unprecedented levels. This survey aims at systematically reviewing the progress of NeRF-based models in computer vision. We begin by explaining the core principles underlying the success of NeRF. Then, we delve into and analyze seven representative NeRF-based representation forms, including Implicit Representation, Neural Point Cloud, and others. Next, we provide a comprehensive comparison and analysis of 14 major research directions that enhance NeRF, such as Modeling Different Practical Capturing Scenarios, Generalization in Modeling, and Modeling Dynamic Scenes. In addition, we conduct both qualitative and quantitative evaluations of numerous NeRF-based methods on multiple datasets, comparing training time, rendering speed, and memory requirements. Finally, we discuss potential future research directions and challenges in this field. We hope that this work will inspire further interest and contribute to advancing the application and development of NeRF in computer vision.