Joint camera pose and dense geometry estimation from a set of images or a
monocular video remains a challenging problem due to its computational
complexity and inherent visual ambiguities. Most dense incremental
reconstruction systems operate directly on image pixels and solve for their 3D
positions using multi-view geometry cues. Such pixel-level approaches suffer
from ambiguities or violations of multi-view consistency (e.g. caused by
textureless or specular surfaces).
We address this issue with a new image representation which we call a
SuperPrimitive. SuperPrimitives are obtained by splitting images into
semantically correlated local regions and enhancing them with estimated surface
normal directions, both of which are predicted by state-of-the-art single image
neural networks. This provides a local geometry estimate per SuperPrimitive,
while their relative positions are adjusted based on multi-view observations.
We demonstrate the versatility of our new representation by addressing three
3D reconstruction tasks: depth completion, few-view structure from motion, and
monocular dense visual odometry.

从一组图像或单目视频中联合估计相机姿态和密集几何，由于其计算复杂性和固有的视觉歧义，仍然是一个具有挑战性的问题。我们通过一种新的图像表示方法 ——SuperPrimitives 来解决这个问题，这些 SuperPrimitives 是通过将图像分割为语义相关的局部区域并利用最先进的单幅图像神经网络预测的表面法线方向来增强的。这提供了每个 SuperPrimitive 的局部几何估计，而它们的相对位置则根据多视角观察进行调整。我们通过解决深度补全、少视角的结构运动和单目密集视觉里程计三个三维重建任务展示了我们新表示方法的多功能性。