In this work we present a novel approach to recover objects 3D position and occupancy in a generic scene using only 2D object detections from multiple view images. The method reformulates the problem as the estimation of a quadric (ellipsoid) in 3D given a set of 2D ellipses fitted to the object detection bounding boxes in multiple views. We show that a closed-form solution exists in the dual-space using a minimum of three views while a solution with two views is possible through the use of non-linear optimisation and object constraints on the size of the object shape. In order to make the solution robust toward inaccurate bounding boxes, a likely occurrence in object detection methods, we introduce a data preconditioning technique and a non-linear refinement of the closed form solution based on implicit subspace constraints. Results on synthetic tests and on different real datasets, involving challenging scenarios, demonstrate the applicability and potential of our method in several realistic scenarios.

中文翻译：

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通过多视图图像检测进行 3D 对象定位


在这项工作中，我们提出了一种新颖的方法，仅使用多视图图像中的 2D 对象检测来恢复通用场景中对象的 3D 位置和占用情况。该方法将问题重新表述为在给定一组适合多个视图中的对象检测边界框的 2D 椭圆的情况下对 3D 二次曲面（椭圆体）进行估计。我们证明，使用至少三个视图的双空间中存在封闭形式的解决方案，而通过使用非线性优化和对对象形状大小的对象约束，可以使用两个视图的解决方案。为了使解决方案对不准确的边界框（对象检测方法中可能出现的情况）具有鲁棒性，我们引入了数据预处理技术和基于隐式子空间约束的封闭形式解决方案的非线性细化。综合测试和不同真实数据集（涉及具有挑战性的场景）的结果证明了我们的方法在几个现实场景中的适用性和潜力。