Volume rendering is an effective method to visualize complex physical features in large-scale scientific data with high expressiveness, the difficulties in processing huge amount of data and capturing complex features, however, are still a great challenge to volume rendering. To address the challenges and improve efficiency and effect of volume rendering, researchers conducted in-depth research on volume rendering algorithms from three aspects. On the one hand, it is an effective way to improve the efficiency of volume rendering by sharing computation with lots of processor cores so as to reduce the computational amount of one processor core. On the other hand, by fully exploring the intrinsic characteristics of three-dimensional data fields, data reduction methods can greatly decrease the amount of data in the rendering process and thus reduce the overhead of a volume rendering algorithm. In addition, feature analysis and enhancement techniques can also be integrated into volume rendering algorithms, thus complex physical features are highlighted from the data fields and high-quality rendering of scientific data is achieved. A survey of recent advances on volume rendering techniques was presented and various research methods were analyzed. In the end, this paper makes prospection for future research directions on volume rendering of large-scale scientific data, including application-driven feature volume rendering, feature-based data reduction in volume rendering, hardware-adapted multi-level acceleration of volume rendering and in-situ intelligent volume rendering.