MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes

Understand

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.