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==3D Scene Understanding== | ==3D Scene Understanding== | ||
What can you get from knowing pairwise pixel distances? (i.e. given two sets of pixels, which pair is closer in 3D space) | |||
You can get horizons. | |||
Single Image Reconstruction | |||
By finding vanishing points and lines, you can do 3D reconstruction. | |||
;Taxonomy | |||
How: Bottom up classifiers to explicit constraints and reasoning. | |||
What: Qualitative to explicit/quantitative. | |||
From qualitative to quantitative: | |||
* Surface labels | |||
* Boundaries + objects | |||
* Stronger geometric constraints | |||
* Reasoning on aspects & poses | |||
* 3D point clouds | |||
Using depth ordering, surface labels, and occlusion cues can give us a planar reconstruction. | |||
Benefits of volumes: | |||
* Finite volumes | |||
* Spatial exclusion (no intersections) | |||
* Mechanical relationships and physical stability (one volume atop another) | |||
Room layout estimation: | |||
* Estimate walls and floor from vanishing points. | |||
* Three principle directions | |||
* Every room is a box | |||
* Minimum number of walls is 1, maximum is 6 but most see 5 walls if camera is facing one wall. | |||
* Use geometric context, optimizing to get a room context. | |||
* Given segmentation masks, you can estimate clutter vs free space. | |||
Functional constraints: | |||
* People sit on laptops, people can open drawer, ... | |||
Primitives | |||
* Depth | |||
* Surface normals | |||
==Objects + 3D== | |||
==Will be on the exam== | ==Will be on the exam== |