Summary of Fine-grained Pillar Feature Encoding Via Spatio-temporal Virtual Grid For 3d Object Detection, by Konyul Park et al.
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection
by Konyul Park, Yecheol Kim, Junho Koh, Byungwoo Park, Jun Won Choi
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the development of efficient architectures for 3D object detection in LiDAR-based systems. The focus is on pillar-based methods that are suitable for onboard deployment due to their computational efficiency. However, these methods can underperform compared to point encoding techniques like Voxel-encoding or PointNet++. To improve performance, a novel pillar encoding architecture called Fine-Grained Pillar Feature Encoding (FG-PFE) is introduced. FG-PFE uses Spatio-Temporal Virtual (STV) grids to capture the distribution of points within each pillar across vertical, temporal, and horizontal dimensions. The encoded features are then aggregated through an Attentive Pillar Aggregation method. Experimental results on the nuScenes dataset show that FG-PFE outperforms baseline models like PointPillar, CenterPoint-Pillar, and PillarNet with only a minor increase in computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For autonomous vehicles to be commercially successful, high-performance 3D object detectors are needed. To make them work on the vehicle itself, not just a powerful computer, we need efficient methods that don’t use too much power. One kind of method is called pillar-based, and it’s good because it’s fast. But sometimes these methods aren’t as good as others, like Voxel-encoding or PointNet++. The problem is that they don’t capture the details within each pillar. This paper introduces a new way to encode pillars called Fine-Grained Pillar Feature Encoding (FG-PFE). FG-PFE uses a special grid to look at points in different ways, then puts them together. It works better than other methods on a big dataset and only takes a little more power. |
Keywords
» Artificial intelligence » Object detection
