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arXiv · 2312.05889

SuperPrimitive: Scene Reconstruction at a Primitive Level

Kirill Mazur·Gwangbin Bae·Andrew J. Davison
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Abstract

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.

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Kirill Mazur, Gwangbin Bae, Andrew J. Davison. 2023-12-10. SuperPrimitive: Scene Reconstruction at a Primitive Level. https://arxiv.org/abs/2312.05889

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