Summary of Unleashing the Power Of Data Synthesis in Visual Localization, by Sihang Li et al.
Unleashing the Power of Data Synthesis in Visual Localization
by Sihang Li, Siqi Tan, Bowen Chang, Jing Zhang, Chen Feng, Yiming Li
First submitted to arxiv on: 28 Nov 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 This paper addresses the long-standing challenge of visual localization, which estimates a camera’s pose within a known scene. Recent end-to-end methods have shown promise for fast inference, but often struggle to generalize to unseen views. The authors propose a novel approach that leverages data synthesis to promote generalizability in pose regression. They lift real 2D images into 3D Gaussian Splats and use these synthetic images as training data. A two-branch joint training pipeline is built, with an adversarial discriminator to bridge the synthetic-to-real gap. Experimental results on established benchmarks show that this method outperforms state-of-the-art end-to-end approaches, reducing errors by up to 50% on indoor datasets and 38.7% on outdoor datasets. The authors also validate their method’s effectiveness in dynamic driving scenarios under varying weather conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making cameras better at knowing where they are within a scene. It’s a big problem that has been around for a long time, and lots of people have tried to solve it. Some new methods have worked well, but they often get stuck when trying to recognize things they haven’t seen before. The authors came up with a clever idea: what if we create fake images that look like real ones, but are actually just made-up scenes? By using these fake images as training data, the camera can learn to recognize more things and do a better job of knowing where it is. |
Keywords
» Artificial intelligence » Inference » Regression
