Reducing Complexity of 3D Indoor Object Detection

Abstract

This work deals with the problem of amodal perception of 3D object detection in indoor environments. We revisited a novel method of 3D object detection [4], in terms of complexity and runtime speed. 3D detection regards not only objects localization in the 3D world, but also estimating their physical sizes and poses, even if only parts of them are visible in the RGB-D image. By following the 2.5D representation approach, the system under study achieves a better mean average precision in detection (40.1%) with respect to all recent methods, but the complexity of the system is very high and, at the moment, its implementation doesn't fit in a small device with low resources in memory and computation. We revisited the referenced system through a variation in its network architecture by introducing a well-adapted and “fine-tuned” MobileNet from Google, with the goal of reducing the complexity of the whole system. Considerable reduction in complexity, computational cost (MAC operations) and memory requirements have been achieved. Many detected classes showed an acceptable level of accuracy and also the speed of the recognition system increased. Final experiments have been conducted on NYUV2 dataset.