Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

Updates

27/04/2023

Initial commits:

1. Pretrained models on Structured3D are provided.
2. The supported code for Semantic Segmentation on ScanNet and S3DIS are provided.

Introduction

We present a pretrained 3D backbone, named Swin3D, that first-time outperforms all state-of-the-art methods on downstream 3D indoor scene understanding tasks. Our backbone network is based on a 3D Swin transformer and carefully designed for efficiently conducting self-attention on sparse voxels with a linear memory complexity and capturing the irregularity of point signals via generalized contextual relative positional embedding. Based on this backbone design, we pretrained a large Swin3D model on a synthetic Structured3D dataset that is 10 times larger than the ScanNet dataset and fine-tuned the pretrained model on various downstream real-world indoor scene understanding tasks.

Overview

• Data Preparation
• Pretrained Models
• Quick Start
• Results and models
• Citation

Data Preparation

We pretrained our Swin3D on Structured3D, please refer to this link to prepare the data.

Pretrained Models

The models pretrained on Structured3D with different cRSE are provided here.

	Pretrain	#params	cRSE	mIoU(val)	Model	Log
Swin3D-S	Structured3D	23.57M	XYZ,RGB	77.69	model	log
Swin3D-S	Structured3D	23.57M	XYZ,RGB,NORM	79.15	model	log
Swin3D-L	Structured3D	60.75M	XYZ,RGB	79.79	model	log
Swin3D-L	Structured3D	60.75M	XYZ,RGB,NORM	81.04	model	log

Quick Start

Install the package using

pip install -r requirements.txt
python setup.py install

Build models and load our pretrained weight, Then you can finetune your model in various task.

import torch
from Swin3D.models import Swin3DUNet
model = Swin3DUNet(depths, channels, num_heads, \
        window_sizes, quant_size, up_k=up_k, \
        drop_path_rate=drop_path_rate, num_classes=num_classes, \
        num_layers=num_layers, stem_transformer=stem_transformer, \
        upsample=upsample, first_down_stride=down_stride, \
        knn_down=knn_down, in_channels=in_channels, \
        cRSE='XYZ_RGB_NORM', fp16_mode=1)
model.load_pretrained_model(ckpt_path)

Results and models

To reproduce our results on downstream tasks, please follow the code in this repo. The results are provided here.

ScanNet Segmentation

	Pretrained	mIoU(Val)	mIoU(Test)
Swin3D-S	✗	75.2	-
Swin3D-S	✓	75.6(76.8)	-
Swin3D-L	✓	76.2(77.5)	77.9

S3DIS Segmentation

	Pretrained	Area 5 mIoU	6-fold mIoU
Swin3D-S	✗	72.5	76.9
Swin3D-S	✓	73.0	78.2
Swin3D-L	✓	74.5	79.8

ScanNet 3D Detection

	Pretrained	mAP@0.25	mAP@0.50
Swin3D-S+FCAF3D	✓	74.2	59.5
Swin3D-L+FCAF3D	✓	74.2	58.6
Swin3D-S+CAGroup3D	✓	76.4	62.7
Swin3D-L+CAGroup3D	✓	76.4	63.2

S3DIS 3D Detection

	Pretrained	mAP@0.25	mAP@0.50
Swin3D-S+FCAF3D	✓	69.9	50.2
Swin3D-L+FCAF3D	✓	72.1	54.0

Citation

If you find Swin3D useful to your research, please cite our work:

@misc{yang2023swin3d,
      title={Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding}, 
      author={Yu-Qi Yang and Yu-Xiao Guo and Jian-Yu Xiong and Yang Liu and Hao Pan and Peng-Shuai Wang and Xin Tong and Baining Guo},
      year={2023},
      eprint={2304.06906},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}