Summary of Tempbev: Improving Learned Bev Encoders with Combined Image and Bev Space Temporal Aggregation, by Thomas Monninger et al.
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation
by Thomas Monninger, Vandana Dokkadi, Md Zafar Anwar, Steffen Staab
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 proposed paper develops a novel approach to improve the accuracy of autonomous driving systems by fusing data from multiple sensors, specifically cameras with different views. The strategy is based on Learned Bird’s-Eye View (BEV) encoders that map sensor data into a joint latent space. To further enhance accuracy, the approach aggregates sensor information over time, which is crucial for monocular camera systems lacking explicit depth and velocity measurements. The paper analyzes existing BEV encoders and their performance in aggregating temporal information, highlighting the complementary strengths of image and BEV latent spaces. A novel temporal BEV encoder, TempBEV, is developed that integrates aggregated temporal information from both latent spaces. Empirical evaluation on the NuScenes dataset shows a significant improvement by TempBEV over the baseline for 3D object detection and BEV segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous cars need to see their surroundings accurately. One way to do this is by combining data from different cameras. This can be done using “Bird’s-Eye View” (BEV) encoders that map camera images into a single space. By combining information over time, the accuracy of the system can be improved. This is especially important for cars that only use one camera because they don’t have explicit depth and velocity measurements like other sensors do. The paper looks at existing BEV encoders and how well they work together. It then develops a new way to combine this information, called TempBEV, that does better than the old methods. This is shown by testing it on a real dataset. |
Keywords
» Artificial intelligence » Encoder » Latent space » Object detection
