Multi-View 3D Object Detection Network for Autonomous Driving

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 10.3% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

Semantic annotation, publication, and discovery of Java software components: an integrated approach

Component-based software development has matured into standard practice in software engineering. Among the advantages of reusing software modules are lower costs, faster development, more manageable code, increased productivity, and improved software quality. As the number of available software components has grown, so has the need for effective component search and retrieval. Traditional search approaches, such as keyword matching, have proved ineffective when applied to software components. Applying a semantically- enhanced approach to component classification, publication, and discovery can greatly increase the efficiency of searching and retrieving software components. This has been already applied in the context of Web technologies, and Web services in particular, in the frame of Semantic Web Services research. This paper examines the similarities between software components and Web services and adapts an existing Semantic Web Service publication and discovery solution into a software component annotation and discovery tool which is implemented as an Eclipse plug-in

Supporting Semantically Enhanced Web Service Discovery for Enterprise Application Integration

The availability of sophisticated Web service discovery mechanisms is an essential prerequisite for increasing the levels of efficiency and automation in EAI. In this chapter, we present an approach for developing service registries building on the UDDI standard and offering semantically-enhanced publication and discovery capabilities in order to overcome some of the known limitations of conventional service registries. The approach aspires to promote efficiency in EAI in a number of ways, but primarily by automating the task of evaluating service integrability on the basis of the input and output messages that are defined in the Web service’s interface. The presented solution combines the use of three technology standards to meet its objectives: OWL-DL, for modelling service characteristics and performing fine-grained service matchmaking via DL reasoning, SAWSDL, for creating semantically annotated descriptions of service interfaces, and UDDI, for storing and retrieving syntactic and semantic information about services and service providers