Online Global Non-rigid Registration for 3D Object Reconstruction Using Consumer-level Depth Cameras

We investigate how to obtain high-quality 360-degree 3D reconstructions of small objects using consumer-level depth cameras. For many homeware objects such as shoes and toys with dimensions around 0.06 – 0.4 meters, their whole projections, in the hand-held scanning process, occupy fewer than 20% pixels of the camera's image. We observe that existing 3D reconstruction algorithms like KinectFusion and other similar methods often fail in such cases even under the close-range depth setting. To achieve high-quality 3D object reconstruction results at this scale, our algorithm relies on an online global non-rigid registration, where embedded deformation graph is employed to handle the drifting of camera tracking and the possible nonlinear distortion in the captured depth data. We perform an automatic target object extraction from RGBD frames to remove the unrelated depth data so that the registration algorithm can focus on minimizing the geometric and photogrammetric distances of the RGBD data of target objects. Our algorithm is implemented using CUDA for a fast non-rigid registration. The experimental results show that the proposed method can reconstruct high-quality 3D shapes of various small objects with textures.