  We present a novel method to infer, in closed-form, a general 3D spatial
occupancy and orientation of a collection of rigid objects given 2D image
detections from a sequence of images. In particular, starting from 2D ellipses
fitted to bounding boxes, this novel multi-view problem can be reformulated as
the estimation of a quadric (ellipsoid) in 3D. We show that an efficient
solution exists in the dual-space using a minimum of three views while a
solution with two views is possible through the use of regularization. However,
this algebraic solution can be negatively affected in the presence of gross
inaccuracies in the bounding boxes estimation. To this end, we also propose a
robust ellipse fitting algorithm able to improve performance in the presence of
errors in the detected objects. Results on synthetic tests and on different
real datasets, involving real challenging scenarios, demonstrate the
applicability and potential of our method.
