Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Fast 3D mapping by matching planes extracted from range sensor point-clouds
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Fast registration based on noisy planes with unknown correspondences for 3-D mapping
IEEE Transactions on Robotics
Sparse non-linear least squares optimization for geometric vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
RGB-D camera-based parallel tracking and meshing
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
Discrete-continuous optimization for large-scale structure from motion
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Relative bundle adjustment based on trifocal constraints
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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Given a hand-held RGB-D camera (e.g. Kinect), methods such as Structure from Motion (SfM) and Iterative Closest Point (ICP), perform poorly when reconstructing indoor scenes with few image features or little geometric structure information. In this paper, we propose to extract high level primitives---planes---from an RGB-D camera, in addition to low level image features (e.g. SIFT), to better constrain the problem and help improve indoor 3D reconstruction. Our work has two major contributions: first, for frame to frame matching, we propose a new scheme which takes into account both low-level appearance feature correspondences in RGB image and high-level plane correspondences in depth image. Second, in the global bundle adjustment step, we formulate a novel error measurement that not only takes into account the traditional 3D point re-projection errors, but also the planar surface alignment errors. We demonstrate with real datasets that our method with plane constraints achieves more accurate and more appealing results comparing with other state-of-the-art scene reconstruction algorithms in aforementioned challenging indoor scenarios.