**Object Classification** Task ==== Various objects are one-by-one placed into the [standard setup](/doc/setup) at different positions and orientations. For each of these frames, the object in the scene must be identified. Compared to the other challenges where complex object properties such as its shape, texture or position are to be reconstructed the answer in each frame here is just one out of eleven object identifiers. However, due to the varying placements in the scene, this challenge is still far from being easy. The objects were scaled to equal surface area to prevent classifying by the size of the object, i.e. amount of reflected light. This approach is not necessarily perfect, as very compact or concave objects appear smaller when scaled by their surface area, but it is a decent compromise. Dataset =======  The dataset contains the eleven following objects: Object name | Material ---------------|--------- Cat | diffuse Icosphere | diffuse LetterG | diffuse Parallelepiped | diffuse Plant | diffuse SpoonDiffuse | diffuse Whale | diffuse Gramophone | specular Headphones | specular Pan | specular SpoonSpecular | specular Each object is placed at eight different positions in the scene, with a varying orientation. So for a total of 88 frames the correct object must be identified. **Ground truth** geometric information is given for all objects. It is therefore a classification of known objects. The spoon is contained twice in the dataset, where the only difference is the material. This tests object classification purely by material and not shape. [](/database/Classification/Classification.7z)**Database** [