Real-time High Resolution Fusion of Depth Maps on GPU
This work addresses the problem of real-time 3D scanning for applications like robotics or VR, but it is incremental as it builds on existing volumetric fusion methods with optimizations.
The paper presents a real-time system for high-quality surface reconstruction from a single moving depth camera on commodity hardware, achieving high accuracy and real-time frame rates by using GPU computing with OpenCL and a sparse volumetric representation.
A system for live high quality surface reconstruction using a single moving depth camera on a commodity hardware is presented. High accuracy and real-time frame rate is achieved by utilizing graphics hardware computing capabilities via OpenCL and by using sparse data structure for volumetric surface representation. Depth sensor pose is estimated by combining serial texture registration algorithm with iterative closest points algorithm (ICP) aligning obtained depth map to the estimated scene model. Aligned surface is then fused into the scene. Kalman filter is used to improve fusion quality. Truncated signed distance function (TSDF) stored as block-based sparse buffer is used to represent surface. Use of sparse data structure greatly increases accuracy of scanned surfaces and maximum scanning area. Traditional GPU implementation of volumetric rendering and fusion algorithms were modified to exploit sparsity to achieve desired performance. Incorporation of texture registration for sensor pose estimation and Kalman filter for measurement integration improved accuracy and robustness of scanning process.