The self-supervised learning-based depth and visual odometry (VO) estimators trained on monocular videos without ground truth have drawn significant attention recently. Prior works use photometric consistency as supervision, which is fragile under complex realistic environments due to illumination variations. More importantly, it suffers from scale inconsistency in the depth and pose estimation results. In this paper, robust geometric losses are proposed to deal with this problem. Specifically, we first align the scales of two reconstructed depth maps estimated from the adjacent image frames, and then enforce forward-backward relative pose consistency to formulate scale-consistent geometric constraints. Finally, a novel training framework is constructed to implement the proposed losses. Extensive evaluations on KITTI and Make3D datasets demonstrate that, i) by incorporating the proposed constraints as supervision, the depth estimation model can achieve state-of-the-art (SOTA) performance among the self-supervised methods, and ii) it is effective to use the proposed training framework to obtain a uniform global scale VO model.
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples, which are usually designed artificially to fool DNNs, but rarely exist in real-world scenarios. In this paper, we study the adversarial examples caused by raindrops, to demonstrate that there exist plenty of natural phenomena being able to work as adversarial attackers to DNNs. Moreover, we present a new approach to generate adversarial raindrops, denoted as AdvRD, using the generative adversarial network (GAN) technique to simulate natural raindrops. The images crafted by our AdvRD look very similar to the real-world raindrop images, statistically close to the distribution of true raindrop images, and more importantly, can perform strong adversarial attack to the state-of-the-art DNN models. On the other side, we show that the adversarial training using our AdvRD images can significantly improve the robustness of DNNs to the real-world raindrop attacks. Extensive experiments are carried out to demonstrate that the images crafted by Ad-vRD are visually and statistically close to the natural raindrop images, can work as strong attackers to DNN models, and also help improve the robustness of DNNs to raindrop attacks.
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