Summary of Extrapolated Urban View Synthesis Benchmark, by Xiangyu Han et al.
Extrapolated Urban View Synthesis Benchmark
by Xiangyu Han, Zhen Jia, Boyi Li, Yan Wang, Boris Ivanovic, Yurong You, Lingjie Liu, Yue Wang, Marco Pavone, Chen Feng, Yiming Li
First submitted to arxiv on: 6 Dec 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 paper presents a significant advancement in photorealistic simulators for vision-centric autonomous vehicles (AVs). It focuses on Novel View Synthesis (NVS), which generates unseen viewpoints to accommodate the pose distribution of AVs. The researchers leverage publicly available AV datasets with multiple traversals, vehicles, and cameras to build the first Extrapolated Urban View Synthesis (EUVS) benchmark. They conduct quantitative and qualitative evaluations of state-of-the-art NVS methods across different evaluation settings, revealing that current methods are prone to overfitting to training views. The paper highlights the need for more robust approaches and large-scale training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a simulator for self-driving cars by generating new views from existing images. They used real data of cities and roads to train their model, which then produced photorealistic views from different angles. This is important because it helps the car see what’s around it, even if it has never seen those exact views before. |
Keywords
» Artificial intelligence » Overfitting
