This paper proposes a generalizable, end-to-end deep learning-based method for relative pose regression between two images. Given two images of the same scene captured from different viewpoints, our algorithm predicts the relative rotation and translation between the two respective cameras. Despite recent progress in the field, current deep-based methods exhibit only limited generalization to scenes not seen in training. Our approach introduces a network architecture that extracts a grid of coarse features for each input image using the pre-trained LoFTR network. It subsequently relates corresponding features in the two images, and finally uses a convolutional network to recover the relative rotation and translation between the respective cameras. Our experiments indicate that the proposed architecture can generalize to novel scenes, obtaining higher accuracy than existing deep-learning-based methods in various settings and datasets, in particular with limited training data.

In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates the light reflected from the surface towards the camera. The geometry is represented as a zero level-set of a neural network, while the neural renderer, derived from the rendering equation, is capable of (implicitly) modeling a wide set of lighting conditions and materials. We trained our network on real world 2D images of objects with different material properties, lighting conditions, and noisy camera initializations from the DTU MVS dataset. We found our model to produce state of the art 3D surface reconstructions with high fidelity, resolution and detail.

通常，根据某些固有的价值衡量标准，绩效是定义的。相反，我们考虑一个个人的价值为\ emph {相对}的设置：当决策者（DM）选择一组从人口中的个人来最大化预期效用时，自然考虑\ emph {预期的边际贡献}（每个人的emc）。我们表明，这个概念满足了这种环境公平性的公理定义。我们还表明，对于某些政策结构，这种公平概念与最大化的预期效用保持一致，而对于线性实用程序功能，它与Shapley值相同。但是，对于某些自然政策，例如选择具有一组特定属性的个人的政策（例如，大学入学的足够高考试成绩），精英级和公用事业最大化之间存在权衡。我们根据挪威大学的大学录取和成果，分析了限制对政策对效用和公平性的影响。