Visual Learning and Recognition: Difference between revisions

Visual Learning and Recognition (view source)

813 bytes added , 17 November 2020

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 * Depth
 * Surface normals
+===Scene Intrinsics===
+Recovering Intrinsic Scene Characteristics:
+Given the following:
+* original scene
+* distance (depth)
+* reflectance
+* orientation (normal)
+* illumination
+You can extract the scene perfectly.
+Learning ordinal relationships:
+* Which point is closer?
+** This gets you depth for 3D
+* Which point is darker?
+** This gets you reflectance for shading
+Depth vs surface normals:
+* Surface normals are gradient of depth
+* Depth is hard to use due to large discontinuities and unbounded values.
+===Reasoning===
+Qualitative Parse Graph
+* Understanding of 3D support, support surfaces (physics)
+** E.g. lamp is supported by nightstand
+* Dataset: NYU v2
+* Given an image, identify surfaces, then classify edges as concave (pop in) or convex (pop out).
+** From this, you can create a popup scene.
 ==Objects + 3D==