Washington RGB-D

Introduced by Kevin Lai et al. in A large-scale hierarchical multi-view RGB-D object dataset

Washington RGB-D is a widely used testbed in the robotic community, consisting of 41,877 RGB-D images organized into 300 instances divided in 51 classes of common indoor objects (e.g. scissors, cereal box, keyboard etc). Each object instance was positioned on a turntable and captured from three different viewpoints while rotating.

Source: Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work


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