  Ground-truth depth, when combined with color data, helps improve object
detection accuracy over baseline models that only use color. However, estimated
depth does not always yield improvements. Many factors affect the performance
of object detection when estimated depth is used. In this paper, we
comprehensively investigate these factors with detailed experiments, such as
using ground-truth vs. estimated depth, effects of different state-of-the-art
depth estimation networks, effects of using different indoor and outdoor RGB-D
datasets as training data for depth estimation, and different architectural
choices for integrating depth to the base object detector network. We propose
an early concatenation strategy of depth, which yields higher mAP than previous
works' while using significantly fewer parameters.
