Image-based rendering (IBR) technique enables presenting real scenes interactively to viewers and hence is a key component for implementing VR telepresence. The quality of IBR results depends on the set of pre-captured views, the rendering algorithm used, and the camera parameters of the novel view to be synthesized. Numerous methods were proposed for optimizing the set of captured images and enhancing the rendering algorithms. However, from which regions IBR methods can synthesize satisfactory results is not yet well studied. In this work, we introduce the concept of renderability, which predicts the quality of IBR results at any given viewpoint and view direction. Consequently, the renderability values evaluated for the 5D camera parameter space form a field, which effectively guides viewpoint/trajectory selection for IBR, especially for challenging large-scale 3D scenes. To demonstrate this capability, we designed 2 VR applications: a path planner that allows users to navigate through sparsely captured scenes with controllable rendering quality and a view selector that provides an overview for a scene from diverse and high quality perspectives. We believe the renderability concept, the proposed evaluation method, and the suggested applications will motivate and facilitate the use of IBR in various interactive settings.

Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this article, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.

In recent decades, unmanned aerial vehicles (UAVs) have developed rapidly and been widely applied in many domains, including photography, reconstruction, monitoring, and search and rescue. In such applications, one key issue is path and view planning, which tells UAVs exactly where to fly and how to search. With specific consideration for three popular UAV applications (scene reconstruction, environment exploration, and aerial cinematography), we present a survey that should assist researchers in positioning and evaluating their works in the context of existing solutions. /Conclusions It should also help newcomers and practitioners in related fields quickly gain an overview of the vast literature. In addition to the current research status, we analyze and elaborate on advantages, disadvantages, and potential explorative trends for each application domain.