We consider the problem of building high-quality 3D object models from commodity RGB and depth sensors. Applications of such a database include instance and object recognition, robot grasping, virtual reality, graphics, and online shopping. Unfortunately, modern reconstruction approaches have difficulties in reconstructing objects with major transparencies (e.g., KinectFusion [22]) and/or concavities (e.g., visual hull). This paper presents a method to fuse visual hull information from off-the-shelf RGB cameras and KinectFusion cues from commodity depth sensors to produce models that are substantially better than either approach on its own. Extensive experiments on the recently published BigBIRD dataset [25] demonstrate that our reconstructions recover more accurate shape and detail than competing approaches, particularly on challenging objects with transparencies and/or concavities. Quantitative evaluations indicate that our approach consistently outperforms competing methods and achieves under 2 mm RMS error. We plan to release our code after the review process.