  This paper addresses the issue on how to more effectively coordinate the
depth with RGB aiming at boosting the performance of RGB-D object detection.
Particularly, we investigate two primary ideas under the CNN model: property
derivation and property fusion. Firstly, we propose that the depth can be
utilized not only as a type of extra information besides RGB but also to derive
more visual properties for comprehensively describing the objects of interest.
So a two-stage learning framework consisting of property derivation and fusion
is constructed. Here the properties can be derived either from the provided
color/depth or their pairs (e.g. the geometry contour adopted in this paper).
Secondly, we explore the fusion method of different properties in feature
learning, which is boiled down to, under the CNN model, from which layer the
properties should be fused together. The analysis shows that different semantic
properties should be learned separately and combined before passing into the
final classifier. Actually, such a detection way is in accordance with the
mechanism of the primary neural cortex (V1) in brain. We experimentally
evaluate the proposed method on the challenging dataset, and have achieved
state-of-the-art performance.
