A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Improved Occupancy Grids for Map Building
Autonomous Robots
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Editor's Choice Article: Video-based, real-time multi-view stereo
Image and Vision Computing
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
A complete statistical inverse ray tracing approach to multi-view stereo
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We present a probabilistic, online, depth map fusion framework, whose generative model for the sensor measurement process accurately incorporates both long-range visibility constraints and a spatially varying, probabilistic outlier model. In addition, we propose an inference algorithm that updates the state variables of this model in linear time each frame. Our detailed evaluation compares our approach against several others, demonstrating and explaining the improvements that this model offers, as well as highlighting a problem with all current methods: systemic bias.