This chapter focuses on quality assessment and perception models for point clouds. It begins with subjective quality assessment, addressing the selection of point cloud content, the introduction of distortion, rendering schemes, and viewing protocols, along with methods for displaying and scoring point clouds. This foundational discussion paves the way for understanding visual attention mechanisms and attention modeling. Subsequently, this chapter examines Just-Noticeable-Distortion (JND) specific to point clouds and relevant datasets, highlighting practical applications in point cloud processing and quality assessment. Finally, it introduces the concept of visual attention modeling, encompassing saliency detection and salient object detection, to elucidate how the human visual system extracts critical information from complex scenes. Overall, this chapter provides significant insights into the research and development of quality assessment and perception models for point cloud technologies.