Summary of Quark: Real-time, High-resolution, and General Neural View Synthesis, by John Flynn et al.
Quark: Real-time, High-resolution, and General Neural View Synthesis
by John Flynn, Michael Broxton, Lukas Murmann, Lucy Chai, Matthew DuVall, Clément Godard, Kathryn Heal, Srinivas Kaza, Stephen Lombardi, Xuan Luo, Supreeth Achar, Kira Prabhu, Tiancheng Sun, Lynn Tsai, Ryan Overbeck
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 This novel neural algorithm enables high-quality, high-resolution, and real-time novel view synthesis from sparse input RGB images or video streams. The feed-forward network reconstructs the 3D scene and renders novel views at 1080p resolution at 30fps on an NVIDIA A100. It generalizes across various datasets and scenes, producing state-of-the-art quality for a real-time method that approaches or surpasses offline methods in some cases. The algorithm builds upon previous works using semi-transparent layers and iterative learned render-and-refine approaches to improve those layers. Instead of flat layers, the method reconstructs layered depth maps (LDMs) efficiently representing scenes with complex depth and occlusions. The architecture includes multi-scale UNet-style components with Transformer-based networks aggregating information from multiple input views. This allows efficient processing at reduced resolution. To achieve real-time rates, the algorithm dynamically creates and discards internal 3D geometry for each frame, generating LDMs for each view. Through extensive evaluation, the algorithm demonstrates state-of-the-art quality at real-time rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This novel algorithm enables high-quality and real-time view synthesis from sparse input images or video streams. It reconstructs the 3D scene and renders novel views in real-time. The feed-forward network generalizes across various datasets and scenes, producing great results. The algorithm uses a combination of concepts to achieve this, including semi-transparent layers and iterative learned render-and-refine approaches. It’s all about creating a cohesive and effective algorithm for view synthesis. |
Keywords
» Artificial intelligence » Transformer » Unet
