Summary of Towards Realistic Scene Generation with Lidar Diffusion Models, by Haoxi Ran et al.
Towards Realistic Scene Generation with LiDAR Diffusion Models
by Haoxi Ran, Vitor Guizilini, Yue Wang
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 LiDAR Diffusion Models (LiDMs) aim to generate realistic LiDAR scenes by incorporating geometric priors into the learning pipeline. The method targets pattern realism, geometry realism, and object realism, introducing curve-wise compression for simulating real-world LiDAR patterns, point-wise coordinate supervision for learning scene geometry, and patch-wise encoding for a full 3D object context. This results in competitive performance on unconditional LiDAR generation and state-of-the-art performance on conditional LiDAR generation while maintaining efficiency. The proposed method enables controllability with various conditions such as semantic maps, camera views, and text prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiDAR Diffusion Models are a new way to create realistic pictures of 3D scenes from point clouds. Right now, these models can’t easily make LiDAR scenes look like real-world scenes because they don’t understand the special patterns and shapes that LiDAR sensors find in the world. To fix this problem, scientists created a new type of model called LiDMs that includes special features to help it learn how to create realistic LiDAR scenes. The model has three main parts: one that helps it make patterns look like real-world patterns, another that teaches it about geometry and shapes, and the last part that helps it understand what objects are in a scene. This new method works really well for making both unconditional (just generating something) and conditional (making something specific happen) LiDAR scenes. |
Keywords
» Artificial intelligence » Diffusion
