Semantic Segmentation of 3D Point Clouds Using Deep Learning

Abstract

Arguably, one of the deep learning’s greatest achievements is transfer learning. It is proven that if we use a rich source dataset to pre-train a network (ImageNet in 2D) and after we use a smaller target dataset to be fine-tuned, it can help to boost performance. It has been used in many applications including language and vision. In 3D scene understanding, there have been few works using this method. Because annotating 3D data is difficult. In this work, we aim to further facilitate research on 3D representation learning. To achieve this goal, we select different datasets and downstream tasks to find the effectiveness of unsupervised pre training on a large and rich source dataset of a 3D scene. The results we obtain are encouraging. we are using a unified backbone, source dataset, and contrastive objective for unsupervised pre-training, and further supervised downstream tasks are performed. Our method is achieving the almost same result in half time of recent works in both pre-training and downstream tasks like semantic segmentation, and instance segmentation.