Efficient Point Cloud Rasterization for Real Time Volumetric Integration in Mixed Reality Applications

Real-time capable simultaneous localization and mapping (SLAM) approaches applying consumer hardware have been extensively researched in recent years. Their 3D reconstruction typically applies voxel volumes stored in regular grid hierarchies, sparse voxel octrees or voxel hash tables. They represent the model implicitly in the form of a truncated signed distance function (TSDF). Data integration is usually achieved by stepping through the reconstruction hierarchy from top to bottom and checking voxel grids against the new input data or by rasterizing input data to find associated voxels. For hierarchical representations, a major challenge remains the efficient determination of relevant portions of the reconstruction to be modified by new input data. We present a novel approach efficiently rasterizing input point clouds into intermediate volumes by the GPU. Our technique performs a simple preprocessing step on the input data to properly account for the TSDF representation, allowing for an accurate and hole-free reconstruction. We show that our approach is well suited for a fast integration of new input data into the hierarchical 3D reconstruction, allowing for real-time performance while only slightly increasing memory consumption.