Camera calibration helps users better interact with the surrounding environments. In this work, we aim at accelerating camera calibration in an indoor setting, by selecting a small but sufficient set of keypoints. Our framework consists of two phases: In the offline phase, we cluster photos labeled with Wi-Fi and gyro sensor data according to a learned distance metric. Photos in each cluster form a “co-scene”. We further select a few frequently appearing keypoints in each co-scene as “useful keypoints” (UKPs). In the online phase, when a query is issued, only UKPs from the nearest co-scene are selected, and subsequently we infer extrinsic camera parameters with multiple view geometry (MVG) technique. Experimental results show that our framework is effective and efficient to support calibration.