Summary of Gsrender: Deduplicated Occupancy Prediction Via Weakly Supervised 3d Gaussian Splatting, by Qianpu Sun et al.
GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting
by Qianpu Sun, Changyong Shu, Sifan Zhou, Zichen Yu, Yan Chen, Dawei Yang, Yuan Chun
First submitted to arxiv on: 19 Dec 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 A novel method for 3D occupancy perception, GSRender, is proposed to simplify the sampling process and improve accuracy. By employing 3D Gaussian Splatting, GSRender naturally handles occupancy prediction, leveraging recent advancements in real-time reconstruction. The approach mitigates limitations of 2D supervision by introducing a Ray Compensation (RC) module to account for features from adjacent frames. A redesigned loss function eliminates the impact of dynamic objects. Experimental results demonstrate state-of-the-art performance in RayIoU (+6.0), narrowing the gap with 3D supervision methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GSRender is a new way to predict what’s around us in 3D. It uses a special technique called Gaussian Splatting, which helps make predictions more accurate and efficient. The method also solves a problem where nearby objects can look like they’re duplicate copies. To do this, GSRender has a special “compensating” feature that looks at what’s happening just before and after the current moment. This makes it better than previous methods at understanding what’s really there. |
Keywords
» Artificial intelligence » Loss function
