Summary of Genmm: Geometrically and Temporally Consistent Multimodal Data Generation For Video and Lidar, by Bharat Singh et al.
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR
by Bharat Singh, Viveka Kulharia, Luyu Yang, Avinash Ravichandran, Ambrish Tyagi, Ashish Shrivastava
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 approach, GenMM, is a novel method for jointly editing RGB videos and LiDAR scans by inserting temporally and geometrically consistent 3D objects. This technique uses a reference image and 3D bounding boxes to seamlessly insert and blend new objects into target videos. The method involves inpainting 2D Regions of Interest (consistent with 3D boxes) using a diffusion-based video inpainting model, computing semantic boundaries of the object, estimating its surface depth, and employing geometry-based optimization to recover the 3D shape of the object’s surface. Finally, LiDAR rays intersecting with the new object surface are updated to reflect consistent depths with its geometry. GenMM demonstrates effectiveness in inserting various 3D objects across video and LiDAR modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing a game where you need to add new characters or objects to the environment. This paper talks about a way to do just that – but for videos and LiDAR scans, which are like special cameras that help robots and self-driving cars see their surroundings. The method uses pictures and 3D shapes to make sure the new objects fit in perfectly with the old ones. It’s like a puzzle where all the pieces match up. The paper shows that this approach works well for adding different types of objects to videos and LiDAR scans. |
Keywords
* Artificial intelligence * Diffusion * Optimization
