This paper proposes a method to complete the missing 3D point cloud reconstructed from aerial multi-view images by using a deep learning method with self-attention. The advancement of drone technology has made it easier to acquire aerial multi-view images. While it is possible to generate 3D point clouds of the terrain by applying 3D photogrammetric techniques to these images, when capturing multi-view aerial images with a drone, high-altitude vertical shooting is often necessary for privacy protection. For example, some portions of the generated 3D point clouds are lost due to shadowed areas caused by roofs and eaves. To address this issue, this research proposes a method to complete the missing 3D point cloud by using a deep learning. In order to obtain accurate and sufficient amount of training data, 3D CG building models are used for generating sets of missing 3D point cloud data and their corresponding Ground Truth. In the experiment, we applied our method to a 3D point cloud generated from actual captured aerial multi-view images and confirmed that the point cloud with a reasonable shape for the missing parts are successfully completed.

Neural radiance fields (NeRFs) have been proposed as methods of novel view synthesis and have been used to address various problems because of its versatility. NeRF can represent colors and densities in 3D space using neural rendering assuming a straight light path. However, a medium with a different refractive index in the scene, such as a transparent medium, causes light refraction and breaks the assumption of the straight path of light. Therefore, the NeRFs cannot be learned consistently across multi-view images. To solve this problem, this study proposes a method to learn consistent radiance fields across multiple viewpoints by introducing the light refraction effect as an offset from the straight line originating from the camera center. The experimental results quantitatively and qualitatively verified that our method can interpolate viewpoints better than the conventional NeRF method when considering the refraction of transparent objects.

To establish advanced analytical methods for preserving cultural heritage, this research proposes a method to generate a time-lapse image with a super-long temporal interval. The key issue is to realize an image collection method using crowdsourcing and a method to improve the matching accuracy between images of cultural heritage buildings captured 50 to 100 years ago and current images. As degradation and damage to the appearance of cultural heritage buildings occurs due to ageing, rebuilding, and renovation, image features of the timed images are changed. This decreases the accuracy of the matching process that uses the appearance of patch-region. In addition, we need to give more consideration to incorrect feature correspondence that is prominent in buildings with considerable symmetry. We aim to solve these difficulties by applying an Autoencoder and a guided matching method. Our method involves utilizing the function of crowdsourcing, which can easily obtain the current image captured at the same position and orientation as the past image. We propose this method to address the inability to obtain the correspondence points between two images when observation times are significantly different.

This work explores the human visual exploratory activity (VEA) in a microgravity environment compared to one-G. Parabolic flights are the only way to experience microgravity without astronaut training, and the duration of each microgravity segment is less than 20 seconds. Under such special conditions, the test subject visually searches a virtual representation of the International Space Station located in his Field of Regard (FOR). The task was repeated in two different postural positions. Interestingly, the test subject reported a significant reduction of microgravity-related motion sickness while experiencing the VR simulation, in comparison to his previous parabolic flights without VR.

In recent years, research efforts have enabled using the internet in disaster areas, and information technology (IT) is expected to improve our comprehension and evaluation of disasters. In disaster areas, crowdsourcing can be employed to secure human resources and controlled-task distribution. In crowdsourcing, many workers with publicly defined tasks (also called microtasks) are used. A simplified and more efficient microtask framework is required for disaster areas due to the lack of human resources and facilities. In this paper, we describe a method that incorporates information collection from a disaster area into microtasks with machine processing support.

In recent years, the pillars of the World Heritage Angkor Thom Bayon temple have become a problem of deterioration due to moss breeding. We aim to generate an image to support observation of moss breeding on a pillar. Even under environment that prevent image processing, we can achieve accurate overlay processing by combining corresponding points between images and 3D shapes. In order to generate the timelapse image of the observation target, many accurate images of different capturing timings are necessary. We are going to use a lot of images collected by crowdsourcing for time lapse images. In this research, we use two crowdsourcing models with the "capturing image of the target region" and the "classification of the captured images" as the micro task. Therefore, image acquisition using crowdsourcing and generation of time lapse image are looped. Time lapse image will be more accurate by repeating this flow.

This paper proposes a calibration method for a large-scale floor projection system at a school gym. In our system, the projectors are installed on the ceiling of a school gym, and the contents are projected onto the floor. The projection results suffer from both projective distortion and lens distortion. It is hard to use chessboard-based calibration methods or self-calibration methods in our system due to the restrictive deploy environment and practical limitations. Our method is basing on the projector-camera system and "straight lines have to be straight". The experiments show that our method fulfills the on-site requirements of the floor projection system at school gym for learning aid usages.

We propose a calibration method for deploying a large-scale projection-based floor display system. In our system, multiple projectors are installed on the ceiling of a large indoor space like a gymnasium to achieve a large projection area on the floor. The projection results suffer from both perspective distortion and lens distortion. In this paper, we use projector-camera systems, in which a camera is mounted on each projector, with the “straight lines have to be straight” methodology, to calibrate our projection system. Different from conventional approaches, our method does not use any calibration board and makes no requirement on the overlapping among the projections and the cameras' fields of view.

Thanks to the rapid progress of the Internet and mobile devices, information related to disaster areas can be collected through the Internet. To grasp the degree of damage in a disaster situation, the use of crowdsourcing for coordinating the individual efforts (micro tasks) of an enormous number of users (workers) on the Internet has been drawing attention as a means of quickly solving problems. However, the information gathered from the Internet is huge and diverse, so it is difficult to formulate as a crowdsourcing task. This paper proposes a conversion platform for the images of a disaster site photographed by various users as information about the site, integrating the images into a single map using 3D image processing, and providing the map to crowdsourcing as a micro task.

Since over one million tourists annually visit the Angkor ruins, the effect on the buildings from the vibrations caused by these tourists is a huge problem for maintaining them. Such organisms as bryophytes, which adhere to the surface of the stones of the ruins, is another factor that damages them. Using crowdsourcing and 3D reconstruction technology, we are organizing a proactive preservation project for the Angkor Thom Bayon Temple, which is a world cultural heritage site. We evaluated its damaged parts and visualized the damaged state.