Designers often express their intents (e.g., on product functionalities and semantics) through shape features. Therefore, collecting such "salient" features from existing shapes and learning their associations with design intents will enable efficient design of new shapes. However, the acquisition of saliency knowledge from a large shape collection has not been accomplished. This paper investigates a gamification approach to this end. In addition, we propose to validate a derived saliency map by its corresponding shape recognition accuracy through crowdsourcing. This allows a comparison across existing and the proposed saliency acquistion and computation methods. Currentl results show that the proposed method achieves statistically similar recognition accuracy to existing saliency data on a standard shape database, indicating that various saliency maps are equally valid according to the proposed saliency definition. Nonetheless, the saliency data obtained through the proposed game consistently produces reasonable viewpoints across shapes, outperforming existing curvature-based and crowdsourcing approaches. The findings from this study could lead to developments of game mechanisms that are more scalable and cost effective at saliency elicitation than existing paid crowdsourcing approaches.