In recent years, camera- and lidar-based 3D object detection has achieved great progress. However, the related researches mainly focus on normal illumination conditions; the performance of their 3D detection algorithms will decrease under low lighting scenarios such as in the night. This work attempts to improve the fusion strategies on 3D vehicle detection accuracy in multiple lighting conditions. First, distance and uncertainty information is incorporated to guide the “painting” of semantic information onto point cloud during the data preprocessing. Moreover, a multitask framework is designed, which incorporates uncertainty learning to improve detection accuracy under low-illumination scenarios. In the validation on KITTI and Dark-KITTI benchmark, the proposed method increases the vehicle detection accuracy on the KITTI benchmark by 1.35% and the generality of the model is validated on the proposed Dark-KITTI dataset, with a gain of 0.64% for vehicle detection.