论文标题
以对象为中心的多视图聚合
Object-Centric Multi-View Aggregation
论文作者
论文摘要
我们提出了一种汇总对象稀疏视图的方法，以便以体积特征网格的形式计算半图表3D表示。我们方法的关键是一个以对象为中心的规范3D坐标系系统，可以将视图提升，而无需明确的摄像头姿势估计，然后组合 - 以一种可以容纳可变的视图并独立视图订单的方式。我们表明，计算从像素到规范坐标系的对称性意识映射使我们能够更好地传播信息以看不见的区域，并在推理过程中坚强地克服姿势歧义。我们的汇总表示使我们能够执行3D推理任务，例如体积重建和新型视图综合，我们使用这些任务来证明与隐式或以摄像头或以摄像头或相机为中心的替代方案相比，我们的聚合方法的好处。
We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid. Key to our approach is an object-centric canonical 3D coordinate system into which views can be lifted, without explicit camera pose estimation, and then combined -- in a manner that can accommodate a variable number of views and is view order independent. We show that computing a symmetry-aware mapping from pixels to the canonical coordinate system allows us to better propagate information to unseen regions, as well as to robustly overcome pose ambiguities during inference. Our aggregate representation enables us to perform 3D inference tasks like volumetric reconstruction and novel view synthesis, and we use these tasks to demonstrate the benefits of our aggregation approach as compared to implicit or camera-centric alternatives.
