Abstract
We develop a 3D object detection algorithm that uses
latent support surfaces to capture contextual relationships
in indoor scenes. Existing 3D representations for RGB-D
images capture the local shape and appearance of object
categories, but have limited power to represent objects with
different visual styles. The detection of small objects is also
challenging because the search space is very large in 3D
scenes. However, we observe that much of the shape variation within 3D object categories can be explained by the
location of a latent support surface, and smaller objects
are often supported by larger objects. Therefore, we explicitly use latent support surfaces to better represent the
3D appearance of large objects, and provide contextual
cues to improve the detection of small objects. We evaluate
our model with 19 object categories from the SUN RGB-D
database, and demonstrate state-of-the-art performance