Summary of Generative Lidar Editing with Controllable Novel Object Layouts, by Shing-hei Ho et al.
Generative LiDAR Editing with Controllable Novel Object Layouts
by Shing-Hei Ho, Bao Thach, Minghan Zhu
First submitted to arxiv on: 30 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 Medium Difficulty summary: We propose a novel framework for editing real-world LiDAR scans to create new scenarios while preserving a realistic background environment. Our approach focuses on generating new object layouts within a given background, providing labeled data for algorithm development and evaluation. Unlike synthetic data generation frameworks, our method ensures generated data remains relevant to specific environments. Compared to novel view synthesis, our framework allows creation of counterfactual scenarios with significant object layout changes without relying on multi-frame optimization. Our pipeline uses generative background inpainting, object point cloud completion, and spherical voxelization to correctly realize LiDAR projective geometry. The generated scans demonstrate realistic object layout changes, benefiting the development of LiDAR-based self-driving systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine having a special tool to edit real-world maps and create new scenarios while keeping the background the same. Our team has developed a way to do just that! We can change the objects on the map (like adding or removing buildings) and keep the rest of the map looking realistic. This is helpful for developing technology like self-driving cars. We compared our method to others and showed that it works well and generates realistic maps with changes. |
Keywords
» Artificial intelligence » Optimization » Synthetic data
