Achieving Flexible 3D Reconstruction Volumes for RGB-D and RGB Camera Based Approaches

Recently, quite a number of approaches came up to reconstruct 3D volumes from RGB or RGBD camera input. However, most of these approaches are rather inflexible regarding the initial camera position with respect of the reconstruction volume and the overall size of the area to be reconstructed. This severly limits the usability of those approaches. In this work we present a flexible approach to store and dynamically extend the reconstruction volume overcoming those problems. We show that our approach additionally requires significantly less memory due to a pyramid-based data storage. We demonstrate that our approach is real-time capable when implemented using the GPU and by that provides a flexible alternative to data structures used in previous approaches.

[1]  Charles T. Loop,et al.  Real-time high-resolution sparse voxelization with application to image-based modeling , 2013, HPG '13.

[2]  John J. Leonard,et al.  Kintinuous: Spatially Extended KinectFusion , 2012, AAAI 2012.

[3]  Andrew J. Davison,et al.  DTAM: Dense tracking and mapping in real-time , 2011, 2011 International Conference on Computer Vision.

[4]  Dieter Fox,et al.  DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Andrew J. Davison,et al.  Live dense reconstruction with a single moving camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Matthias Nießner,et al.  Real-time 3D reconstruction at scale using voxel hashing , 2013, ACM Trans. Graph..

[7]  Björn E. Ottersten,et al.  Kinect Deform: Enhanced 3D Reconstruction of Non-rigidly Deforming Objects , 2014, 2014 2nd International Conference on 3D Vision.

[8]  John J. Leonard,et al.  Real-time large-scale dense RGB-D SLAM with volumetric fusion , 2014, Int. J. Robotics Res..

[9]  Olaf Kähler,et al.  Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices , 2015, IEEE Transactions on Visualization and Computer Graphics.

[10]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[11]  Daniel Cremers,et al.  Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Ming Zeng,et al.  Octree-based fusion for realtime 3D reconstruction , 2013, Graph. Model..

[13]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[14]  Jiawen Chen,et al.  Scalable real-time volumetric surface reconstruction , 2013, ACM Trans. Graph..

[15]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[16]  Tim Weyrich,et al.  Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion , 2013, 2013 International Conference on 3D Vision.