  Accurate 3D object detection with LiDAR is critical for autonomous driving.
Existing research is all based on the flat-world assumption. However, the
actual road can be complex with steep sections, which breaks the premise.
Current methods suffer from performance degradation in this case due to
difficulty correctly detecting objects on sloped terrain. In this work, we
propose Det6D, the first full-degree-of-freedom 3D object detector without
spatial and postural limitations, to improve terrain robustness. We choose the
point-based framework by founding their capability of detecting objects in the
entire spatial range. To predict full-degree poses, including pitch and roll,
we design a ground-aware orientation branch that leverages the local ground
constraints. Given the difficulty of long-tail non-flat scene data collection
and 6D pose annotation, we present Slope-Aug, a data augmentation method for
synthesizing non-flat terrain from existing datasets recorded in flat scenes.
Experiments on various datasets demonstrate the effectiveness and robustness of
our method in different terrains. We further conducted an extended experiment
to explore how the network predicts the two extra poses. The proposed modules
are plug-and-play for existing point-based frameworks. The code is available at
https://github.com/HITSZ-NRSL/De6D.
