  Convolutional neural network (CNN) based architectures, such as Mask R-CNN,
constitute the state of the art in object detection and segmentation. Recently,
these methods have been extended for model-based segmentation where the network
outputs the parameters of a geometric model (e.g. an ellipse) directly. This
work considers objects whose three-dimensional models can be represented as
ellipsoids. We present a variant of Mask R-CNN for estimating the parameters of
ellipsoidal objects by segmenting each object and accurately regressing the
parameters of projection ellipses. We show that model regression is sensitive
to the underlying occlusion scenario and that prediction quality for each
object needs to be characterized individually for accurate 3D object
estimation. We present a novel ellipse regression loss which can learn the
offset parameters with their uncertainties and quantify the overall geometric
quality of detection for each ellipse. These values, in turn, allow us to fuse
multi-view detections to obtain 3D ellipsoid parameters in a principled
fashion. The experiments on both synthetic and real datasets quantitatively
demonstrate the high accuracy of our proposed method in estimating 3D objects
under heavy occlusions compared to previous state-of-the-art methods.
