Probabilistic Structure From Motion With Objects (Psfmo)

This paper proposes a probabilistic approach to recover affine camera calibration and objects position/occupancy from multi-view images using solely the information from image detections. We show that remarkable object localisation and volumetric occupancy can be recovered by including both geometrical constraints and prior information given by objects CAD models from the ShapeNet dataset. This can be done by recasting the problem in the context of a probabilistic framework based on PPCA that enforces both geometrical constraints and the associated semantic given by the object category extracted by the object detector. We present results on synthetic data and extensive real evaluation on the ScanNet datasets on more than 1200 image sequences to show the validity of our approach in realistic scenarios. In particular, we show that 3D statistical priors are key to obtain reliable reconstruction especially when the input detections are noisy, a likely case in real scenes.