  Our goal is to develop stable, accurate, and robust semantic scene
understanding methods for wide-area scene perception and understanding,
especially in challenging outdoor environments. To achieve this, we are
exploring and evaluating a range of related technology and solutions, including
AI-driven multimodal scene perception, fusion, processing, and understanding.
This work reports our efforts on the evaluation of a state-of-the-art approach
for semantic segmentation with multiple RGB and depth sensing data. We employ
four large datasets composed of diverse urban and terrain scenes and design
various experimental methods and metrics. In addition, we also develop new
strategies of multi-datasets learning to improve the detection and recognition
of unseen objects. Extensive experiments, implementations, and results are
reported in the paper.
