  3D-LaneNet+ is a camera-based DNN method for anchor free 3D lane detection
which is able to detect 3d lanes of any arbitrary topology such as splits,
merges, as well as short and perpendicular lanes. We follow recently proposed
3D-LaneNet, and extend it to enable the detection of these previously
unsupported lane topologies. Our output representation is an anchor free,
semi-local tile representation that breaks down lanes into simple lane segments
whose parameters can be learnt. In addition we learn, per lane instance,
feature embedding that reasons for the global connectivity of locally detected
segments to form full 3d lanes. This combination allows 3D-LaneNet+ to avoid
using lane anchors, non-maximum suppression, and lane model fitting as in the
original 3D-LaneNet. We demonstrate the efficacy of 3D-LaneNet+ using both
synthetic and real world data. Results show significant improvement relative to
the original 3D-LaneNet that can be attributed to better generalization to
complex lane topologies, curvatures and surface geometries.
