  3D object detection plays a crucial role in numerous intelligent vision
systems. Detection in the open world inevitably encounters various adverse
scenes, such as dense fog, heavy rain, and low light conditions. Although
existing efforts primarily focus on diversifying network architecture or
training schemes, resulting in significant progress in 3D object detection,
most of these learnable modules fail in adverse scenes, thereby hindering
detection performance. To address this issue, this paper proposes a monocular
3D detection model designed to perceive twin depth in adverse scenes, termed
MonoTDP, which effectively mitigates the degradation of detection performance
in various harsh environments. Specifically, we first introduce an adaptive
learning strategy to aid the model in handling uncontrollable weather
conditions, significantly resisting degradation caused by various degrading
factors. Then, to address the depth/content loss in adverse regions, we propose
a novel twin depth perception module that simultaneously estimates scene and
object depth, enabling the integration of scene-level features and object-level
features. Additionally, we assemble a new adverse 3D object detection dataset
encompassing a wide range of challenging scenes, including rainy, foggy, and
low light weather conditions, with each type of scene containing 7,481 images.
Experimental results demonstrate that our proposed method outperforms current
state-of-the-art approaches by an average of 3.12% in terms of AP_R40 for car
category across various adverse environments.
