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In recent years, novel view synthesis from a single image has seen significant progress thanks to the rapid advancements in 3D scene representation and image inpainting techniques. While the current approaches are able to synthesize geometrically consistent novel views, they often do not handle the view-dependent effects properly. Specifically, the highlights in their synthesized images usually appear to be glued to the surfaces, making the novel views unrealistic. To address this major problem, we make a key observation that the process of synthesizing novel views requires changing the shading of the pixels based on the novel camera, and moving them to appropriate locations. Therefore, we propose to split the view synthesis process into two independent tasks of pixel reshading and relocation. During the reshading process, we take the single image as the input and adjust its shading based on the novel camera. This reshaded image is then used as the input to an existing view synthesis method to relocate the pixels and produce the final novel view image. We propose to use a neural network to perform reshading and generate a large set of synthetic input-reshaded pairs to train our network. We demonstrate that our approach produces plausible novel view images with realistic moving highlights on a variety of real world scenes. 

Autonomous vehicles (AVs) are on the verge of changing the transportation industry. Despite the fast development of autonomous driving systems (ADSs), they still face safety and security challenges. Current defensive approaches usually focus on a narrow objective and are bound to specific platforms, making them difficult to generalize. To solve these limitations, we propose AVMaestro, an efficient and effective policy enforcement framework for full-stack ADSs. AVMaestro includes a code instrumentation module to systematically collect required information across the entire ADS, which will then be feed into a centralized data examination module, where users can utilize the global information to deploy defensive methods to protect AVs from various threats. AVMaestro is evaluated on top of Apollo-6.0 and experimental results confirm that it can be easily incorporated into the original ADS with almost negligible run-time delay. We further demonstrate that utilizing the global information can not only improve the accuracy of existing intrusion detection methods, but also potentially inspire new security applications.