#Movie explorer consulting a map movie

These geometric constraints are obtained through a coarse 3D modelisation of the environment, similar to those provided by GIS database. Our solution is based on a postprocessing algorithm that exploits additional geometric constraints, relative to the environment, to correct both the reconstructed geometry and the camera trajectory. In this paper, we propose a solution to reduce those errors a posteriori. However, current SLAM methods are still prone to drift errors, which prevent their use in large-scale applications. It is now possible to follow in real time the trajectory of a moving camera in an unknown environment. In the past few years, lots of works were achieved on Simultaneous Localization and Mapping (SLAM). We show that it outperforms the state-of-the-art technique For the experiments, we firstly built a dataset for the evaluation, and quantitatively evaluate the depth prediction in outdoor scenes. We propose a framework of direct use of ERP with coordinate conversion of correspondences and distortion-aware upsampling module to deal with the ERP related problems and extend a self-supervised learning method for open environments. Different from the existing method, we directly use the ERP format. There is only one method existing that uses cube map projection to produce six perspective images and apply self-supervised learning using motion pictures for perspective depth prediction to deal with these problems. Movie-Map, an interactive first-person-view map that engages the user in a simulated walking experience, comprises short 360 images, that are coordinate and distortion. It can also show virtual billboards in the view. The interface allows for easy exploration of a target area. By connecting the video segments following the specified movement in an area, we can watch a walking view along a street. Turning views at intersections are synthesized. Frames of the video are localized on the map, intersections are detected, and videos are segmented. After acquiring 360° videos along streets in target areas, the analysis of videos is almost automatic. The system consists of four stages acquisition, analysis, management, and interaction.

#Movie explorer consulting a map movie

We build a new Movie Map system with an interface for exploring cities. Thus, we renovate the Movie Map using state-of-the-art technology. However, Aspen Movie Map was based on analog technology with a huge effort and never built again. Movie Map uses videos instead of sparse images and seems to improve the user experience dramatically.

Forty years ago, a video-based interactive map was created - it is well-known as Aspen Movie Map. Despite the wide use of GSV, it provides sparse images of streets, which often confuses users and lowers user satisfaction. Nowadays, we are familiar with Google Street View (GSV) that is an interactive visual map.

Visual exploration of a city is always needed. This paper introduces our work on a Movie Map, which will enable users to explore a given city area using 360° videos.