Abstract:

In complex environments, the point cloud obtained directly by RGB-D camera is often affected by complex external scenes, such as light, transparent objects, shadows, etc., resulting in large-scale loss of point cloud, and even being unable to represent the real 3D features of the object. Incomplete point cloud affects many important applications in computer vision fields, such as object detection, path planning, etc. To solve this issue, a method is proposed to complete the point cloud using multimodal data such as RGB image semantic information. This method first uses a semantic segmentation network of RGB image, which is based on the "encoder-decoder" structure to obtain the semantic segmentation results of RGB image, and then takes RGB image, semantic segmentation results, and incomplete sparse depth map as the input of algorithm, and outputs completed point cloud. After a large number of experiments in complex scenes, the experiments show that this method is effective in completion effect and operation efficiency.

Key words: point cloud completion, computer vision, deep learning, semantic segmentation