Accurate localization of other traffic participants is a vital task in
autonomous driving systems. State-of-the-art systems employ a combination of
sensing modalities such as RGB cameras and LiDARs for localizing traffic
participants, but most such demonstrations have been confined to plain roads.
We demonstrate, to the best of our knowledge, the first results for monocular
object localization and shape estimation on surfaces that do not share the same
plane with the moving monocular camera. We approximate road surfaces by local
planar patches and use semantic cues from vehicles in the scene to initialize a
local bundle-adjustment like procedure that simultaneously estimates the pose
and shape of the vehicles, and the orientation of the local ground plane on
which the vehicle stands as well. We evaluate the proposed approach on the
KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations.
The proposed approach significantly improves the state-of-the-art for monocular
object localization on arbitrarily-shaped roads.

本论文提出了一种在具有不同平面的路面上进行单目物体定位和形状估计的方法，并使用 KITTI 和 SYNTHIA-SF 基准测试了该方法，证明了其在任意形状的路面上能显著提高单目物体定位的效果。