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

TL;DR

Det6D introduces a ground-aware 3D object detection method that accurately predicts full 6D poses, including pitch and roll, to enhance terrain robustness in autonomous driving scenarios, especially on complex sloped terrains.

Contribution

This work presents the first full-degree-of-freedom 3D object detector that leverages ground constraints and a novel data augmentation for non-flat terrains, improving detection in complex environments.

Findings

Demonstrates improved detection accuracy on sloped terrains.

Effective augmentation method for non-flat scene data.

Plug-and-play modules compatible with existing frameworks.

Abstract

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.