README.md

Dynamic Graph CNN for Learning on Point Clouds

Introduction

We implement DGCNN and provide the results and checkpoints on S3DIS dataset.

@article{dgcnn,
  title={Dynamic Graph CNN for Learning on Point Clouds},
  author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
  journal={ACM Transactions on Graphics (TOG)},
  year={2019}
}

Notice: We follow the implementations in the original DGCNN paper and a PyTorch implementation of DGCNN code.

Results

S3DIS

Method	Split	Lr schd	Mem (GB)	Inf time (fps)	mIoU (Val set)	Download
DGCNN	Area_1	cosine 100e	13.1		68.33	model | log
DGCNN	Area_2	cosine 100e	13.1		40.68	model | log
DGCNN	Area_3	cosine 100e	13.1		69.38	model | log
DGCNN	Area_4	cosine 100e	13.1		50.07	model | log
DGCNN	Area_5	cosine 100e	13.1		50.59	model | log
DGCNN	Area_6	cosine 100e	13.1		77.94	model | log
DGCNN	6-fold				59.43

Notes:

• We use XYZ+Color+Normalized_XYZ as input in all the experiments on S3DIS datasets.
• Area_5 Split means training the model on Area_1, 2, 3, 4, 6 and testing on Area_5.
• 6-fold Split means the overall result of 6 different splits (Area_1, Area_2, Area_3, Area_4, Area_5 and Area_6 Splits).
• Users need to modify train_area and test_area in the S3DIS dataset's config to set the training and testing areas, respectively.

Indeterminism

Since DGCNN testing adopts sliding patch inference which involves random point sampling, and the test script uses fixed random seeds while the random seeds of validation in training are not fixed, the test results may be slightly different from the results reported above.