From Dusk Till Dawn: Modeling in the Dark
Internet photo collections naturally contain a large variety of illumination conditions, with the largest difference between day and night images. Current modeling techniques do not embrace the broad illumination range often leading to reconstruction failure or severe artifacts. We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information. The proposed method relies on automatic scene appearance grouping, which is used to obtain separate dense 3D models. Subsequent model fusion combines the separate models into a complete and accurate reconstruction of the scene. In addition, we propose a method to derive the appearance information for the model under the different illumination conditions, even for scene parts that are not observed under one illumination condition. To achieve this, we develop a cross-illumination color transfer technique. We evaluate our method on a large variety of landmarks from across Europe reconstructed from a database of 7.4M images.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper propo…
Image SegmentationSegmentationSemantic SegmentationImage Recoloring Based on Object Color Distributions
We present a method to perform automatic image recoloring based on the distribution of colors associated with objects present in an image. For example, when recoloring an image containing a sky object, our method incorpo…
ObjectSegmentationSemantic SegmentationForesee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indi…
Data AugmentationDomain GeneralizationEvolving Domain GeneralizationMeta-LearningPixel Invisibility: Detecting Objects Invisible in Color Images
Despite recent success of object detectors using deep neural networks, their deployment on safety-critical applications such as self-driving cars remains questionable. This is partly due to the absence of reliable estima…
Knowledge Distillationobject-detectionObject DetectionSelf-Driving CarsThe NEOLIX Open Dataset for Autonomous Driving
With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence…
Autonomous Driving