FCAT: Fully Convolutional Network with Self-Attention for Point Cloud based Place Recognition

Abstract

Point cloud-based large-scale place recognition is still challenging due to the difficulty of extracting discriminative local descriptors from an unordered point cloud and integrating them effectively into a robust global descriptor. In this work, we construct a novel network named FCAT (Fully Convolutional network with a self-ATtention unit) that can generate a discriminative and context-aware global descriptor for place recognition from the 3D point cloud. It features with a novel sparse fully convolutional network architecture with sparse tensors for extracting informative local geometric features computed in a single pass. It also involves a self-attention module for 3D point cloud to encode local context information between local descriptors. Thanks to the effectiveness of these two modules, we demonstrate our method mostly outperforms state-of-the-art methods on large-scale place recognition tasks in PointNetVLAD. Moreover, our method shows strong robustness to different weather and light conditions through the experiments on the 6-DoF image-based visual localization task in RobotCar Seasons dataset.

Environment and Dependencies

Code was tested using Python 3.8 with PyTorch 1.7 and MinkowskiEngine 0.4.3 on Ubuntu 18.04 with CUDA 10.2.

The following Python packages are required:

• PyTorch (version 1.7)
• MinkowskiEngine (version 0.4.3)
• pytorch_metric_learning (version 0.9.94 or above)
• tensorboard
• pandas
• psutil
• bitarray

Modify the PYTHONPATH environment variable to include absolute path to the project root folder:

export PYTHONPATH=$PYTHONPATH:/home/.../FCAT

Datasets

FCAT is trained on a subset of Oxford RobotCar and In-house (U.S., R.A., B.D.) datasets introduced in PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition paper [1]. There are two training datasets:

• Baseline Dataset - consists of a training subset of Oxford RobotCar
• Refined Dataset - consists of training subset of Oxford RobotCar and training subset of In-house

For dataset description see PointNetVLAD paper or github repository (link).

You can download training and evaluation datasets from here. Extract the folder in the same directory as the project code. Thus, in that directory you must have two folders: 1) benchmark_datasets and 2) FCAT

Before the network training or evaluation, run the below code to generate pickles with positive and negative point clouds for each anchor point cloud.

cd generating_queries/ 

# Generate training tuples for the Baseline Dataset
python generate_training_tuples_baseline.py

# Generate training tuples for the Refined Dataset
python generate_training_tuples_refine.py

# Generate evaluation tuples
python generate_test_sets.py

Training

To train FCAT network, download and decompress the dataset and generate training pickles as described above. Edit the configuration file (config_baseline.txt or config_refined.txt). Set dataset_folder parameter to the dataset root folder.

To train the network, run:

cd training

# To train FCAT model on the Baseline Dataset
python train.py --config ../config/config_baseline.txt --model_config ../models/fcat.txt

# To train FCAT model on the Refined Dataset
python train.py --config ../config/config_refined.txt --model_config ../models/fcat.txt

Pre-trained Models

Pretrained models are available in weights directory

• fcat_baseline.pth trained on the Baseline Dataset
• fcat_refined.pth trained on the Refined Dataset

Evaluation

To evaluate pretrained models run the following commands:

cd eval

# To evaluate the model trained on the Baseline Dataset
python evaluate.py --config ../config/config_baseline.txt --model_config ../models/fcat.txt --weights ../weights/fcat_baseline.pth

# To evaluate the model trained on the Refined Dataset
python evaluate.py --config ../config/config_refined.txt --model_config ../models/fcat.txt --weights ../weights/fcat_refined.pth

Results

FCAT performance (measured by Average Precision@1%) compared to state-of-the-art:

Trained on Baseline Dataset

Method	Oxford	U.S.	R.A.	B.D
PointNetVLAD [1]	80.3	72.6	60.3	65.3
PCAN [2]	83.8	79.1	71.2	66.8
DH3D-4096 [3]	84.3	-	-	-
DAGC [4]	87.5	83.5	75.7	71.2
LPD-Net [5]	94.9	96.0	90.5	89.1
MinkLoc3D [6]	97.9	95.0	91.2	88.5
FCAT (ours)	98.2	96.4	94.0	91.7

Trained on Refined Dataset

Method	Oxford	U.S.	R.A.	B.D
PointNetVLAD [1]	80.1	94.5	93.1	86.5
PCAN [2]	86.4	94.1	92.3	87.0
DAGC [4]	87.8	94.3	93.4	88.5
LPD-Net [5]	94.9	98.9	96.4	94.4
MinkLoc3D [6]	98.5	99.7	99.3	96.7
FCAT (ours)	98.3	99.8	98.7	96.7

1. M. A. Uy and G. H. Lee, "PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2. W. Zhang and C. Xiao, "PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
3. Du, Juan, Rui Wang, and Daniel Cremers. "DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization." European Conference on Computer Vision (ECCV). Springer, Cham, 2020.
4. Q. Sun et al., "DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition", Proceedings of the 2020 International Conference on Multimedia Retrieval
5. Z. Liu et al., "LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis," 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
6. J. Komorowski, "MinkLoc3D: Point Cloud Based Large-Scale Place Recognition", Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), (2021)