  Traditional 3D mesh saliency detection algorithms and corresponding databases
were proposed under several constraints such as providing limited viewing
directions and not taking the subject's movement into consideration. In this
work, a novel 6DoF mesh saliency database is developed which provides both the
subject's 6DoF data and eye-movement data. Different from traditional
databases, subjects in the experiment are allowed to move freely to observe 3D
meshes in a virtual reality environment. Based on the database, we first
analyze the inter-observer variation and the influence of viewing direction
towards subject's visual attention, then we provide further investigations
about the subject's visual attention bias during observation. Furthermore, we
propose a 6DoF mesh saliency detection algorithm based on the uniqueness
measure and the bias preference. To evaluate the proposed approach, we also
design an evaluation metric accordingly which takes the 6DoF information into
consideration, and extend some state-of-the-art 3D saliency detection methods
to make comparisons. The experimental results demonstrate the superior
performance of our approach for 6DoF mesh saliency detection, in addition to
providing benchmarks for the presented 6DoF mesh saliency database. The
database and the corresponding algorithms will be made publicly available for
research purposes.
