Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene
Suryansh Kumar, Yuchao Dai, Hongdong Li

TL;DR
This paper introduces a unified method for dense 3D reconstruction of complex dynamic scenes from monocular images, modeling scenes as assemblies of rigid planar segments to improve accuracy and address scale ambiguity.
Contribution
The authors propose a novel approach that models dynamic scenes as piecewise planar structures with local rigid motions, simplifying the reconstruction process.
Findings
Achieves state-of-the-art results on benchmark datasets.
Effectively addresses scale ambiguity in structure-from-motion.
Demonstrates robustness on complex dynamic scenes.
Abstract
This work addresses the task of dense 3D reconstruction of a complex dynamic scene from images. The prevailing idea to solve this task is composed of a sequence of steps and is dependent on the success of several pipelines in its execution. To overcome such limitations with the existing algorithm, we propose a unified approach to solve this problem. We assume that a dynamic scene can be approximated by numerous piecewise planar surfaces, where each planar surface enjoys its own rigid motion, and the global change in the scene between two frames is as-rigid-as-possible (ARAP). Consequently, our model of a dynamic scene reduces to a soup of planar structures and rigid motion of these local planar structures. Using planar over-segmentation of the scene, we reduce this task to solving a "3D jigsaw puzzle" problem. Hence, the task boils down to correctly assemble each rigid piece to…
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Taxonomy
MethodsJigsaw
