SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences

TL;DR

SALT is a semi-automatic annotation tool that significantly accelerates labeling of RGB-D videos for 3D object detection and segmentation, reducing annotation time while maintaining high quality.

Contribution

The paper introduces SALT, a novel tool that combines semi-automatic annotation techniques with pre-processing functionalities for efficient RGB-D video labeling.

Findings

Annotation time reduced by up to 33.95 times for bounding boxes.

RGB segmentation annotation time reduced by up to 8.55 times.

High-quality automatic ground truth generation maintained.

Abstract

Large labeled data sets are one of the essential basics of modern deep learning techniques. Therefore, there is an increasing need for tools that allow to label large amounts of data as intuitively as possible. In this paper, we introduce SALT, a tool to semi-automatically annotate RGB-D video sequences to generate 3D bounding boxes for full six Degrees of Freedom (DoF) object poses, as well as pixel-level instance segmentation masks for both RGB and depth. Besides bounding box propagation through various interpolation techniques, as well as algorithmically guided instance segmentation, our pipeline also provides built-in pre-processing functionalities to facilitate the data set creation process. By making full use of SALT, annotation time can be reduced by a factor of up to 33.95 for bounding box creation and 8.55 for RGB segmentation without compromising the quality of the automatically generated ground truth.