  We present a new learning-based framework S-3D-RCNN that can recover accurate
object orientation in SO(3) and simultaneously predict implicit rigid shapes
from stereo RGB images. For orientation estimation, in contrast to previous
studies that map local appearance to observation angles, we propose a
progressive approach by extracting meaningful Intermediate Geometrical
Representations (IGRs). This approach features a deep model that transforms
perceived intensities from one or two views to object part coordinates to
achieve direct egocentric object orientation estimation in the camera
coordinate system. To further achieve finer description inside 3D bounding
boxes, we investigate the implicit shape estimation problem from stereo images.
We model visible object surfaces by designing a point-based representation,
augmenting IGRs to explicitly address the unseen surface hallucination problem.
Extensive experiments validate the effectiveness of the proposed IGRs, and
S-3D-RCNN achieves superior 3D scene understanding performance. We also
designed new metrics on the KITTI benchmark for our evaluation of implicit
shape estimation.
