We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. 3D bounding box parameters are estimated in one pass for every point, aggregated through graph convolutions, and fed into a branch of the network that predicts latent codes representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-to-end training of the 3D object detection pipeline. Thus our model is able to extract shapes without access to ground-truth shape information in the target dataset. During experiments, we find that our proposed method achieves state-of-the-art results by~5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Waymo Open Dataset, while reproducing the shapes of detected cars.

Desktop virtualization is a new delivery method in which desktop operating systems execute in a data center and users access their applications using stateless "thin-client" devices. This paradigm promises significant benefits in terms of data security, flexibility, and reduction of the total cost of ownership. It is important to further improve this approach while maintaining good user experience, decreasing the propelled desktop's granularity and optimizing the protocol utilized between clients and servers. In this paper, we build a feasible cloud computing system, in which application streaming technology is used to break the bottleneck between clients and computing resources, and at the server side, virtualization technology is utilized to help achieve better physical resource management, as well as the high performance virtualized storage system. Simulations show that the proposed system greatly improves the QoS of users and efficiently allocates resources among all clients.