Accurate and real-time 3D pedestrian detection using an efficient attentive pillar network

Research output: Contribution to journalArticleResearchpeer-review


Efficiently and accurately detecting people from 3D point cloud data is of great importance in many robotic and autonomous driving applications. This fundamental perception task is still very challenging due to (i) significant deformations of human body pose and gesture over time and (ii) point cloud sparsity and scarcity for pedestrian objects. Recent efficient 3D object detection approaches rely on pillar features. How-ever, these pillar features do not carry sufficient expressive representations to deal with all the aforementioned challenges in detecting people. To address this shortcoming, we first in-troduce a stackable Pillar Aware Attention (PAA) module to enhance pillar feature extraction while suppressing noises in point clouds. By integrating multi-point-channel-pooling, point-wise, channel-wise, and task-aware attention into a simple module, representation capabilities of pillar features are boosted while only requiring little additional computational resources. We also present Mini-BiFPN, a small yet effective feature network that creates bidirectional information flow and multi-level cross-scale feature fusion to better integrate multi-resolution features. Our proposed framework, namely PiFeNet, has been evaluated on three popular large-scale datasets for 3D pedestrian Detection, i.e. KITTI, JRDB, and nuScenes. It achieves state-of-the-art performance on KITTI Bird-eye-view (BEV) as well as JRDB, and competitive performance on nuScenes. Our approach is a real-time detector with 26 frame-per-second (FPS). The code for our PiFeNet is available at <uri>https:&#x002F;&#x002F;;ldtho&#x002F;PiFeNet</uri>.

Original languageEnglish
Pages (from-to)1159-1166
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - Feb 2023


  • 3D object detection
  • Deep Learning for Visual Perception
  • Detectors
  • Feature extraction
  • LiDAR
  • Object detection
  • Point cloud compression
  • Real-time
  • Real-time systems
  • Recogni-tion
  • Task analysis
  • Three-dimensional displays

Cite this