1 research outputs found
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion
Images of heavily occluded objects in cluttered scenes, such as fruit
clusters in trees, are hard to segment. To further retrieve the 3D size and 6D
pose of each individual object in such cases, bounding boxes are not reliable
from multiple views since only a little portion of the object's geometry is
captured. We introduce the first CNN-based ellipse detector, called Ellipse
R-CNN, to represent and infer occluded objects as ellipses. We first propose a
robust and compact ellipse regression based on the Mask R-CNN architecture for
elliptical object detection. Our method can infer the parameters of multiple
elliptical objects even they are occluded by other neighboring objects. For
better occlusion handling, we exploit refined feature regions for the
regression stage, and integrate the U-Net structure for learning different
occlusion patterns to compute the final detection score. The correctness of
ellipse regression is validated through experiments performed on synthetic data
of clustered ellipses. We further quantitatively and qualitatively demonstrate
that our approach outperforms the state-of-the-art model (i.e., Mask R-CNN
followed by ellipse fitting) and its three variants on both synthetic and real
datasets of occluded and clustered elliptical objects.Comment: 18 pages, 20 figures, 7 table