  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.
