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README.md

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+# SESS: Self-Ensembling Semi-Supervised 3D Object Detection
+Created by <a href="https://github.com/Na-Z" target="_blank">Na Zhao</a> from 
+<a href="http://www.nus.edu.sg/" target="_blank">National University of Singapore</a>
+
+![teaser](https://github.com/Na-Z/sess/blob/master/teaser.pdf)
+
+## Introduction
+This repository contains the PyTorch implementation for our CVPR 2020 Paper 
+"SESS: Self-Ensembling Semi-Supervised 3D Object Detection" by Na Zhao, Tat Seng Chua, Gim Hee Lee (arXiv report 
+[here](https://arxiv.org/abs/1912.11803)).
+
+The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D 
+annotations. However, such annotations are often tedious and expensive to collect. Semi-supervised learning is a good 
+alternative to mitigate the data annotation issue, but has remained largely unexplored in 3D object detection. Inspired 
+by the recent success of self-ensembling technique in semi-supervised image classification task, we propose SESS, a 
+self-ensembling semi-supervised 3D object detection framework. Specifically, we design a thorough perturbation scheme 
+to enhance generalization of the network on unlabeled and new unseen data. Furthermore, we propose three consistency 
+losses to enforce the consistency between two sets of predicted 3D object proposals, to facilitate the learning of 
+structure and semantic invariances of objects. Extensive experiments conducted on SUN RGB-D and ScanNet datasets 
+demonstrate the effectiveness of SESS in both inductive and transductive semi-supervised 3D object detection. Our SESS 
+achieves competitive performance compared to the state-of-the-art fully-supervised method by using only 50% labeled data.
+
+**Code will come soon.**