Visual Learning and Recognition: Difference between revisions

Visual Learning and Recognition (view source)

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 ==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==