MESH SALIENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

Mesh saliency has been widely considered as the measure of visual importance of certain parts of 3D geometries, distinguishable
from their surroundings, with respect to human visual perception. This work is based on the use of convolutional
neural networks to extract saliency maps for large and dense 3D scanned models. The network is trained with
saliency maps extracted by fusing local and global spectral characteristics. Extensive evaluation studies carried out using
various 3D models, include visual perception evaluation in simplification and compression use cases. As a result, they
verify the superiority of our approach as compared to other state-of-the-art approaches. Furthermore, these studies indicate
that CNN-based saliency extraction method is much faster in large and dense geometries, allowing the application
of saliency aware compression and simplification schemes in low-latency and energy-efficient systems.