FragmentFusion: A LightWeight SLAM Pipeline for Dense Reconstruction We present FragmentFusion, a realtime dense reconstruction pipeline that combines sparse camera tracking with imagespace volumetric fusion. The tracking is based on ORBSLAM, which constructs and optimizes a sparse global map of 3D points and keyframes. We transform each of these keyframes into decimated meshes and render them from the estimated viewpoint. Fusion is performed in the pixel shader by exploiting atomic operations on a packed data structure. This eliminates the need of a 3D voxel grid making FragmentFusion very flexible at large scenes and varying scales. Moreover, since all keyframes are fused onthefly, we can use bundle adjustment and loopclosure without expensive volumetric reintegration. FragmentFusion is lightweight in terms of compute power and memory consumption. It can easily fuse several hundreds of keyframes in real time, in a quality comparable to other approaches. We achieve realtime frame rates on a notebook at around 20% CPU and GPU utilization and low memory consumption.

GPU Accelerated Robust Scene Reconstruction We propose a fast and accurate 3D reconstruction system that takes a sequence of RGBD frames and produces a globally consistent camera trajectory and a dense 3D geometry. We redesign core modules of a stateoftheart offline reconstruction pipeline to maximally exploit the power of GPU. We introduce GPU accelerated core modules that include RGBD odometry, geometric feature extraction and matching, point cloud registration, volumetric integration, and mesh extraction. Therefore, while being able to reproduce the results of the highfidelity offline reconstruction system, our system runs more than 10 times faster on average. Nearly 10Hz can be achieved in medium size indoor scenes, making our offline system even comparable to online Simultaneous Localization and Mapping (SLAM) systems in terms of the speed. Experimental results show that our system produces more accurate results than several stateoftheart online systems. The system is open source at https:github.comtheNdedOpen3D.