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Accurate 3D object detection with LiDAR is critical for autonomous driving.Existing research is all based on the flat-world assumption. However, theactual road can be complex with steep sections, which breaks the premise.Current methods suffer from performance degradation in this case due todifficulty correctly detecting objects on sloped terrain. In this work, wepropose Det6D, the first full-degree-of-freedom 3D object detector withoutspatial and postural limitations, to improve terrain robustness. We choose thepoint-based framework by founding their capability of detecting objects in theentire spatial range. To predict full-degree poses, including pitch and roll,we design a ground-aware orientation branch that leverages the local groundconstraints. Given the difficulty of long-tail non-flat scene data collectionand 6D pose annotation, we present Slope-Aug, a data augmentation method forsynthesizing non-flat terrain from existing datasets recorded in flat scenes.Experiments on various datasets demonstrate the effectiveness and robustness ofour method in different terrains. We further conducted an extended experimentto explore how the network predicts the two extra poses. The proposed modulesare plug-and-play for existing point-based frameworks. The code is available athttps://github.com/HITSZ-NRSL/De6D.

Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness