Boundless Reconstruction Using Regularized 3D Fusion

3D reconstruction from image based depth sensor is essential part of many offline or online robotic applications. Numerous techniques have been developed to integrate multiple depth maps to create 3D model of environment, however accuracy of the reconstructed 3D model exclusively depends upon the precision of depth sensing. Economical depth sensors such as Kinect and stereo camera sensors provide imprecise depth data which affect the integration process and produce unwanted noisy surfaces in 3D model. There exist several approaches which use image filtering based depth map denoising, however applying filtering directly on depth data can result in inconsistent and deformed 3D model. In this paper we investigate and extend a recursive variant of total variation based filtering to incorporate multi-view based depth images while applying implicit depth smoothing. Proposed framework uses sparse voxel representation to aid large scale 3D model reconstruction and is shown to reduce absolute surface error of final reconstructed 3D model by up to 77% in comparison with state of the art 3D fusion techniques.

[1]  Susan M. Downes,et al.  A Depth-Based Head-Mounted Visual Display to Aid Navigation in Partially Sighted Individuals , 2013, PloS one.

[2]  Marc Pollefeys,et al.  City-Scale Change Detection in Cadastral 3D Models Using Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[5]  Dirk Baumbach,et al.  Stereo-vision-aided inertial navigation for unknown indoor and outdoor environments , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[6]  Jürgen Wohlfeil,et al.  EXTENSION AND EVALUATION OF THE AGAST FEATURE DETECTOR , 2016 .

[7]  Sebastian Thrun,et al.  Self-supervised Monocular Road Detection in Desert Terrain , 2006, Robotics: Science and Systems.

[8]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[9]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[10]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008 .

[11]  Wolfram Burgard,et al.  3-D Mapping With an RGB-D Camera , 2014, IEEE Transactions on Robotics.

[12]  Daniel Cremers,et al.  Dense visual SLAM for RGB-D cameras , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Anko Börner,et al.  Infinite, Sparse 3D Modelling Volumes , 2016, VISIGRAPP.

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

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

[16]  Daniel Cremers,et al.  Real-Time Dense Geometry from a Handheld Camera , 2010, DAGM-Symposium.

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

[18]  Andrew J. Davison,et al.  A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Olaf Hellwich,et al.  Recursive Total Variation Filtering Based 3D Fusion , 2016, SIGMAP.

[20]  Daniel Cremers,et al.  Volumetric 3D mapping in real-time on a CPU , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Amir Golroo,et al.  KINECT, A NOVEL CUTTING EDGE TOOL IN PAVEMENT DATA COLLECTION , 2015 .