Classical object detection methods only extract the objects' image features via CNN, lack of utilizing the relationship among objects in the same image. In this article, we introduce the graph convolutional networks (GCN) into the object detection field and propose a new framework called OD-GCN (object detection with graph convolutional network). It utilizes the category relationship to improve the detection precision. We set up a knowledge graph to reflect the co-exist relationships among objects. GCN plays the role of postprocessing to adjust the output of base object detection models, so it is a flexible framework that any pre-trained object detection models can be used as the base model. In experiments, we try several popular base detection models. OD-GCN always improve mAP by 1~5pp on COCO dataset. In addition, visualized analysis reveals the benchmark improvement is quite reasonable in human's opinion.

Depth images captured by off-the-shelf RGB-D cameras suffer from much stronger noise than color images. In this paper, we propose a method to denoise the depth images in RGB-D images by color-guided graph filtering. Our iterative method contains two components: color-guided similarity graph construction, and graph filtering on the depth signal. Implemented in graph vertex domain, filtering is accelerated as computation only occurs among neighboring vertices. Experimental results show that our method outperforms state-of-art depth image denoising methods significantly both on quality and efficiency.

No abstract

No abstract

No abstract