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Visual aliasing, or doppelgangers, poses severe challenges to 3D reconstruction. We propose Doppelganger++, an enhanced pairwise image classifier that excels in visual disambiguation across diverse and challenging scenes. We seamlessly integrate Doppelganger++ into SfM, successfully disambiguating each scene. (Middle) Compared to prior work (which we refer to as DG-OG), Doppelgangers++ is more robust for everyday scenes, showing improved accuracy and robustness. We show pairs that DG-OG classifies incorrectly and ours gets correct. Our new VisymScenes dataset, featuring complex daily scenes, is particularly challenging for COLMAP and DG-OG, but our method can achieve correct and complete reconstructions. 

The standard pipeline for many vision tasks uses a conventional camera to capture an image that is then passed to a digital processor for information extraction. In some deployments, such as private locations, the captured digital imagery contains sensitive information exposed to digital vulnerabilities such as spyware, Trojans, etc. However, in many applications, the full imagery is unnecessary for the vision task at hand. In this paper we propose an optical and analog system that preprocesses the light from the scene before it reaches the digital imager to destroy sensitive information. We explore analog and optical encodings consisting of easily implementable operations such as convolution, pooling, and quantization. We perform a case study to evaluate how such encodings can destroy face identity information while preserving enough information for face detection. The encoding parameters are learned via an alternating optimization scheme based on adversarial learning with deep neural networks. We name our system CAnOPIC (Camera with Analog and Optical Privacy-Integrating Computations) and show that it has better performance in terms of both privacy and utility than conventional optical privacy-enhancing methods such as blurring and pixelation.