Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover

Abstract

In this chapter, we present a system for real-time point cloud mapping and scene reconstruction based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). Compared to state-ofthe-art approaches that achieve real-time performance using projective data association schemes which operate on the 3-D scene geometry solely, our method allows to incorporate additional complementary information to guide the registration process. In this work, the ICP's nearest neighbor search evaluates both geometric and photometric information in a direct manner, achieving robust mappings in real-time. In order to overcome the performance bottleneck in nearest neighbor search space traversal, we exploit the inherent computation parallelism of GPUs. In particular, we have adapted the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, to low-dimensional RGB-D data. The system is validated on scene and object reconstruction scenarios. Our implementation achieves frame-to-frame registration runtimes of less than 20 ms on an off-the-shelf consumer GPU.

3-D Operation Situs Reconstruction with Time-of-Flight Satellite Cameras Using Photogeometric Data Fusion

“…In this context, a major contribution is the incorporation of confidence weights derived from ToF characteristics into the TSDF reconstruction. To cope with real-time requirements in medical environments we apply a GPU-based photogeometric registration approach [8]. Below we detail the initial preprocessing for ToF data.…”

“…For estimating the rotation matrix R k and the translation vector t k between the camera coordinate system of frame k and the global world coordinate system we align two successive frames by applying an approximate iterative closest point (ICP) implementation [8]. The approach extends the traditional 3-D nearest neighbor search within ICP to higher dimensions, thus enabling the incorporation of additional complementary information, e.g.…”