Category-independent object detection and localisation plays an important role in many computer vision tasks. In this study, an efficient method is proposed for generic objectness ranking by fusing two dimension (2D) or 3D information. A novel Bayesian model is designed to integrate multimodal cues and global cues to estimate object location, scale and number. In the pure trichannel colour space, the authors employ global spatial information as new global cues. From the colour+depth (red, green and blue+D) aspect, the authors compute multimodal saliency and oversegments to find two new multimodal cues. Local and regional depth cues are also explored and combined with them together so that a reliable objectness ranking scheme can be implemented. The proposed method is evaluated on web-public common 2D and RGB+D datasets. In RGB+D cases, the experimental results show that the proposed method achieves an average 5\% improvement over state-of-the-art methods. Furthermore, for achieving the similar recall rates, the authors' method only needs 30\% amounts of sampled windows with respect of other available methods.