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Low-order photometric parameters of light fields are important aspects determining lighting qualities but difficult to be measured. In this article, we did a systematical investigation into the performance of a cubic meter and a tetrahedron meter as well as an HDR panoramic map-based method in simultaneously recovering light density, direction, and diffuseness. Five metrics were introduced with two based on the measurement using a cubic illumination meter, two based on a tetrahedron shaped illumination meter and one based on SH decomposition of HDR panoramic maps. Furthermore, the measurement of five metrics was simulated under six HDR Panoramic maps of natural scenes in various postures which mimic the complexity of real environments. The results indicate that the measurement of low-order photometric parameters of light fields using Spherical Harmonics (i.e. latter referred to as SH) decomposition of HDR panoramic map gave the most reliable results. The tetrahedron meter based metrics gave more robust results in the measurement of light density while the cubic meter based metrics performed better when measuring light direction. This study also provides a solution for a more robust measurement of light diffuseness based on the ratio between the vector strength measured with a cubic meter and light density measured with a tetrahedron meter. With the quick development of high-speed built-in camera and mobile computing techniques, this research provides confidence in developing applications in mobile phone by capturing HDR panoramic maps or built in light meters to measure the low-order photometric parameters of light fields in 3D spaces.

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Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.