Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Smoothing by Example: Mesh Denoising by Averaging with Similarity-Based Weights
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-view extension of the ICP algorithm
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
L1 rotation averaging using the Weiszfeld algorithm
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Hi-index | 0.00 |
In this paper we describe a system for building geometrically consistent 3D models using structured-light depth cameras. While the commercial availability of such devices, i.e. Kinect, has made obtaining depth images easy, the data tends to be corrupted with high levels of noise. In order to work with such noise levels, our approach decouples the problem of scan alignment from that of merging the aligned scans. The alignment problem is solved by using two methods tailored to handle the effects of depth image noise and erroneous alignment estimation. The noisy depth images are smoothed by means of an adaptive bilateral filter that explicitly accounts for the sensitivity of the depth estimation by the scanner. Our robust method overcomes failures due to individual pairwise ICP errors and gives alignments that are accurate and consistent. Finally, the aligned scans are merged using a standard procedure based on the signed distance function representation to build a full 3D model of the object of interest. We demonstrate the performance of our system by building complete 3D models of objects of different physical sizes, ranging from cast-metal busts to a complete model of a small room as well as that of a complex scale model of an aircraft.