  To identify and fit geometric primitives (e.g., planes, spheres, cylinders,
cones) in a noisy point cloud is a challenging yet beneficial task for fields
such as robotics and reverse engineering. As a multi-model multi-instance
fitting problem, it has been tackled with different approaches including
RANSAC, which however often fit inferior models in practice with noisy inputs
of cluttered scenes. Inspired by the corresponding human recognition process,
and benefiting from the recent advancements in image semantic segmentation
using deep neural networks, we propose BAGSFit as a new framework addressing
this problem. Firstly, through a fully convolutional neural network, the input
point cloud is point-wisely segmented into multiple classes divided by jointly
detected instance boundaries without any geometric fitting. Thus, segments can
serve as primitive hypotheses with a probability estimation of associating
primitive classes. Finally, all hypotheses are sent through a geometric
verification to correct any misclassification by fitting primitives
respectively. We performed training using simulated range images and tested it
with both simulated and real-world point clouds. Quantitative and qualitative
experiments demonstrated the superiority of BAGSFit.
