Segmentation and recognition using structure from motion point clouds

We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our main contribution is to show how semantic segmentation is possible based solely on motion-derived 3D world structure. Our method works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors. Experiments were performed on a challenging new video database containing sequences filmed from a moving car in daylight and at dusk. The results confirm that indeed, accurate segmentation and recognition are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance. © 2008 Springer Berlin Heidelberg.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.

An integrated service-device-technology roadmap for smart city development

Firms and other organizations use Technology Roadmapping (TRM) extensively as a framework for supporting research and development of future technologies and products that could sustain a competitive advantage. While the importance of technology strategy has received more attention in recent years, few research studies have examined how roadmapping processes are used to explore the potential convergence of products and services that may be developed in the future. The aim of this paper is to introduce an integrated roadmapping process for services, devices and technologies capable of implementing a smart city development R&D project in Korea. The paper applies a QFD (Quality Function Deployment) method to establish interconnections between services and devices, and between devices and technologies. The method is illustrated by a detailed case study, which shows how different types of roadmap can be coordinated with each other to produce a clear representation of the technological changes and uncertainties associated with the strategic planning of complex innovations. © 2012 Elsevier Inc.