Learning-Deep-Learning

Pillar-based Object Detection for Autonomous Driving

November 2020

tl;dr: Improved multi-view fusion.

Overall impression

Three key improvements based on MVF. The ablation studies in this paper is super clean and persuasive.

Key ideas

• Multiview architecture
  • Voxelize points in BEV or spherical view or cylindrical view to pillars.
  • Extract pillar features.
  • Project pillar features to points with nearest neighbor or bilinear interpolation and concat to point features.
  • Transform point features to BEV
  • Detection backbone + head
• Anchor-based Pillar-based prediction: like CenterPoint and Pixor.
  • Both PointPillars and MVF uses anchor-based prediction.
  • Anchor free avoids complicated anchor matching strategy.
  • Ablation studies show that anchor-based < point-based « pillar-based.
• Cylindrical view: height z, azimuth angle, radial distance. The radial distance is treated as channels.
  • Cylindrical view is better than spherical view as the vehicle size for distant cars are not distorted. Distant cars appears smaller in spherical view but the same in cylindrical view. –> LaserNet uses a range view (RV) which is very similar to spherical view. The original MVF is also a spherical view.
• Bilinear upsampling when transferring pillar features to point.
  • This avoids the spatial inconsistency and dependency of quantization into diff bins.
  • Bilinear interpolation is better than nearest neighbor. This observation is consistent with the comparison between RoIAlign with RoIPooling.

Technical details

• Positive anchors in lidar BEV is very sparse (< 0.1%).

Notes

• Questions and notes on how to improve/revise the current work