Real-time Preprocessing for Dense 3-D Range Imaging on the GPU: Defect Interpolation, Bilateral Temporal Averaging and Guided Filtering

Recent advances in range imaging (RI) have enabled dense 3-D scene acquisition in real-time. However, due to physical limitations and the underlying range sampling principles, range data are subject to noise and may contain invalid measurements. Hence, data preprocessing is a prerequisite for practical applications but poses a challenge with respect to real-time constraints. In this paper, we propose a generic and modality-independent pipeline for efficient RI data preprocessing on the graphics processing unit (GPU). The contributions of this work are efficient GPU implementations of normalized convolution for the restoration of invalid measurements, bilateral temporal averaging for dynamic scenes, and guided filtering for edge-preserving denoising. Furthermore, we show that the transformation from range measurements to 3-D world coordinates can be computed efficiently on the GPU. The pipeline has been evaluated on real data from a Time-of-Flight sensor and Microsoft's Kinect. In a run-time performance study, we show that for VGA-resolution data, our preprocessing pipeline runs at similar to 100 fps on an off-the-shelf consumer GPU.