Summary of Taming Transformers For Realistic Lidar Point Cloud Generation, by Hamed Haghighi et al.
Taming Transformers for Realistic Lidar Point Cloud Generation
by Hamed Haghighi, Amir Samadi, Mehrdad Dianati, Valentina Donzella, Kurt Debattista
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in Lidar point cloud generation, benefiting from their stable training and iterative refinement during sampling. However, DMs often fail to realistically model Lidar raydrop noise due to their inherent denoising process. This paper introduces LidarGRIT, a generative model that uses auto-regressive transformers to iteratively sample range images in the latent space, rather than image space. Additionally, LidarGRIT utilizes VQ-VAE to separately decode range images and raydrop masks. The results demonstrate that LidarGRIT achieves superior performance compared to SOTA models on KITTI-360 and KITTI odometry datasets. The model’s ability to realistically generate raydrop noise and its potential applications in various fields make it an important contribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about creating a new type of computer program that can generate pictures from Lidar data, which is used to create 3D maps. The program is called LidarGRIT and it’s better than other programs at generating these pictures because it can add realistic noise to the images. Noise is like the random errors that happen when we take a picture with a camera. This program uses special math techniques to make the pictures look more real. The researchers tested their program on two big datasets and it did better than other programs in those tests. This means that LidarGRIT could be used in many different fields, such as self-driving cars or mapping out buildings. |
Keywords
» Artificial intelligence » Diffusion » Generative model » Latent space
