论文标题
LIDARGAIT：用点云的基准测试3D步态识别
LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
论文作者
论文摘要
基于视频的步态识别在限制的情况下取得了令人印象深刻的结果。但是，相机忽略了人类3D结构信息，这限制了步态识别在3D野生世界中的可行性。这项工作没有从图像中提取步态功能，而是探索了点云中精确的3D步态特征，并提出了一个简单而有效的3D步态识别框架，称为Lidargait。我们提出的方法将稀疏点云投影到深度图中，以使用3D几何信息来学习表示形式，该信息的表现优于现有的基于点的方法和基于摄像机的方法。由于缺乏点云数据集，我们构建了第一个基于LIDAR的大规模的步态识别数据集，Sustech1k，该数据集由LIDAR传感器和RGB摄像机收集。该数据集包含来自1,050个受试者的25,239个序列，并涵盖了许多变化，包括可见性，视图，遮挡，衣服，携带，携带和场景。广泛的实验表明，（1）3D结构信息是步态识别的重要特征。 （2）Lidargait的表现优于基于点和轮廓的现有方法，但它也提供了稳定的跨视图结果。 （3）LIDAR传感器优于RGB摄像头，可以在室外环境中进行步态识别。源代码和数据集已在https://lidargait.github.io上提供。
Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we built the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://lidargait.github.io.
