Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)

Related tags

Deep LearningPMF
Overview

Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation (ICCV 2021)

[中文|EN]

概述

本工作主要探索一种高效的多传感器（激光雷达和摄像头）融合点云语义分割方法。现有的多传感器融合方法主要将点云投影到图像上，获取对应的像素位置之后，将对应位置的图像信息投影回点云空间进行特征融合。但是，这种方式下并不能很好的利用图像丰富的视觉感知特征（例如形状、纹理等）。因此，我们尝试探索一种在RGB图像空间进行特征融合的方式，提出了一个基于视觉感知的多传感器融合方法（PMF)。详细内容可以查看我们的公开论文。

image-20211013141408045

主要实验结果

PWC

Leader board of [email protected]

image-20211013144333265

更多实验结果

我们在持续探索PMF框架的潜力，包括探索更大的模型、更好的ImageNet预训练模型、其他的数据集等。我们的实验结果证明了，PMF框架是易于拓展的，并且其性能可以通过使用更好的主干网络而实现提升。详细的说明可以查看文件。

方法 数据集 mIoU (%)
PMF-ResNet34 SemanticKITTI Validation Set 63.9
PMF-ResNet34 nuScenes Validation Set 76.9
PMF-ResNet50 nuScenes Validation Set 79.4
PMF48-ResNet101 SensatUrban Test Set (ICCV2021 Competition) 66.2 (排名 5)

使用说明

注：代码中涉及到包括数据集在内的各种路径配置，请根据自己的实际路径进行修改

代码结构

|--- pc_processor/ 点云处理的Python包
	|--- checkpoint/ 生成实验结果目录
	|--- dataset/ 数据集处理
	|--- layers/ 常用网络层
	|--- loss/ 损失函数
	|--- metrices/ 模型性能指标函数
	|--- models/ 网络模型
	|--- postproc/ 后处理，主要是KNN
	|--- utils/ 其他函数
|--- tasks/ 实验任务
	|--- pmf/ PMF 训练源代码
	|--- pmf_eval_nuscenes/ PMF 模型在nuScenes评估代码
		|--- testset_eval/ 合并PMF以及salsanext结果并在nuScenes测试集上评估
		|--- xxx.py PMF 模型在nuScenes评估代码
	|--- pmf_eval_semantickitti/ PMF 在SemanticKITTI valset上评估代码
	|--- salsanext/ SalsaNext 训练代码，基于官方公开代码进行修改
	|--- salsanext_eval_nuscenes/ SalsaNext 在nuScenes 数据集上评估代码

模型训练

训练任务代码目录结构

|--- pmf/
	|--- config_server_kitti.yaml SemanticKITTI数据集训练的配置脚本
	|--- config_server_nus.yaml nuScenes数据集训练的配置脚本
	|--- main.py 主函数
	|--- trainer.py 训练代码
	|--- option.py 配置解析代码
	|--- run.sh 执行脚本，需要 chmod+x 赋予可执行权限

步骤

  1. 进入 tasks/pmf目录，修改配置文件 config_server_kitti.yaml中数据集路径 data_root 为实际数据集路径。如果有需要可以修改gpu，batch_size等参数
  2. 修改 run.sh 确保 nproc_per_node 的数值与yaml文件中配置的gpu数量一致
  3. 运行如下指令执行训练脚本
./run.sh
# 或者 bash run.sh
  1. 执行成功之后会在 PMF/experiments/PMF-SemanticKitti路径下自动生成实验日志文件，目录结构如下：
|--- log_dataset_network_xxxx/
	|--- checkpoint/ 训练断点文件以及最佳模型参数
	|--- code/ 代码备份
	|--- log/ 控制台输出日志以及配置文件副本
	|--- events.out.tfevents.xxx tensorboard文件

控制台输出内容如下，其中最后的输出时间为实验预估时间

image-20211013152939956

模型推理

模型推理代码目录结构

|--- pmf_eval_semantickitti/ SemanticKITTI评估代码
	|--- config_server_kitti.yaml 配置脚本
	|--- infer.py 推理脚本
	|--- option.py 配置解析脚本

步骤

  1. 进入 tasks/pmf_eval_semantickitti目录，修改配置文件 config_server_kitti.yaml中数据集路径 data_root 为实际数据集路径。修改pretrained_path指向训练生成的日志文件夹目录。
  2. 运行如下命令执行脚本
python infer.py config_server_kitti.yaml
  1. 运行成功之后，会在训练模型所在目录下生成评估结果日志文件，文件夹目录结构如下：
|--- PMF/experiments/PMF-SemanticKitti/log_xxxx/ 训练结果路径
	|--- Eval_xxxxx/ 评估结果路径
		|--- code/ 代码备份
		|--- log/ 控制台日志文件
		|--- pred/ 用于提交评估的文件

引用

@InProceedings{Zhuang_2021_ICCV,
    author    = {Zhuang, Zhuangwei and Li, Rong and Jia, Kui and Wang, Qicheng and Li, Yuanqing and Tan, Mingkui},
    title     = {Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {16280-16290}
}
Owner
ICE
Model compression; Object detection; Point cloud processing;
ICE
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