Gaussian Time Machine: A Real-Time Rendering Methodology for Time-Variant Appearances
(2405.13694)Abstract
Recent advancements in neural rendering techniques have significantly enhanced the fidelity of 3D reconstruction. Notably, the emergence of 3D Gaussian Splatting (3DGS) has marked a significant milestone by adopting a discrete scene representation, facilitating efficient training and real-time rendering. Several studies have successfully extended the real-time rendering capability of 3DGS to dynamic scenes. However, a challenge arises when training images are captured under vastly differing weather and lighting conditions. This scenario poses a challenge for 3DGS and its variants in achieving accurate reconstructions. Although NeRF-based methods (NeRF-W, CLNeRF) have shown promise in handling such challenging conditions, their computational demands hinder real-time rendering capabilities. In this paper, we present Gaussian Time Machine (GTM) which models the time-dependent attributes of Gaussian primitives with discrete time embedding vectors decoded by a lightweight Multi-Layer-Perceptron(MLP). By adjusting the opacity of Gaussian primitives, we can reconstruct visibility changes of objects. We further propose a decomposed color model for improved geometric consistency. GTM achieved state-of-the-art rendering fidelity on 3 datasets and is 100 times faster than NeRF-based counterparts in rendering. Moreover, GTM successfully disentangles the appearance changes and renders smooth appearance interpolation.
Overview
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The Gaussian Time Machine (GTM) introduces a real-time method for rendering 3D scenes that change appearance due to time-variant factors such as weather and lighting, significantly boosting both the quality and speed of rendering.
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GTM employs lightweight neural networks with time embedding vectors to predict the attributes of Gaussian primitives, allowing it to capture dynamic changes in opacity and color over time while maintaining high geometric consistency through a decomposed color model.
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Experimental results on datasets like World Across Time, Phototourism, and NeRF-OSR demonstrate GTM's state-of-the-art rendering fidelity and impressive speed, rendering at 80 FPS, which is 100 times faster than NeRF-based methods.
Gaussian Time Machine: Real-Time Rendering for Varying Appearances
Introduction
Ever wished your photos could capture how a scene changes over time? The "Gaussian Time Machine (GTM)" is here to get you closer to that dream. In their paper, the authors introduce an innovative approach for rendering 3D scenes that change appearance due to time-variant factors like weather or lighting. This method not only boosts the rendering quality but does so in real-time, making it much faster than previous methods.
Background
In 3D rendering and neural rendering, the goal is to create high-fidelity 3D models that can be interactively manipulated and viewed from various angles. Traditional methods could struggle or slow down significantly when handling scenes captured under different conditions. The NeRF (Neural Radiance Fields) methods, for instance, have shown promise in this area but are often too computationally demanding for real-time applications.
Core Contributions
Disentangling Time-Variant Attributes
GTM takes a clever approach by using lightweight neural networks to predict the attributes of Gaussian primitives—basic 3D components that make up the scene. By adding time embedding vectors to the inputs of the model, they managed to capture changes in opacity and color, thus modeling how visibility and appearances dynamically change over time.
Decomposed Color Model
Another interesting point is their decomposed color model. GTM separates the rendering color into static and dynamic components. This ensures that while the geometric consistency remains high, the model can still flexibly adapt to variations in appearance. This enhances both efficiency and the quality of the rendered scene.
Experimental Results
The paper details experiments conducted on three representative datasets: World Across Time (WAT), Phototourism, and NeRF-OSR. The results are impressive:
- Quality of Rendering: GTM achieved state-of-the-art fidelity on all tested datasets.
- Speed: Perhaps more impressively, the rendering speed clocked in at 80 FPS—making it 100 times faster than NeRF-based methods.
| Dataset | PSNR | SSIM | LPIPS | FPS | Disk (MB) | |-|-|-|--||| | WAT | 27.62 | 0.873 | 0.216 | 80.71 | < 100 | | Phototourism | 25.30 | 0.854 | 0.208 | - | - | | NeRF-OSR | 21.27 | 0.741 | 0.294 | 80.71 | - |
Implications
Practical Applications
- Interactive Graphics: With GTM being highly efficient, it can be used in interactive graphics applications, enabling real-time changes in lighting and appearance.
- Digital Twins: Imagining the future of virtual reality experiences, GTM could significantly enhance realism. From virtual tours to digital twins of cities, this tech offers exciting prospects.
Theoretical Implications
From a theoretical perspective, GTM showcases the potential of blending neural networks with traditional 3D rendering techniques to solve complex problems. It proves that decomposing tasks at the neural representation level can significantly improve both speed and quality.
Looking Forward
Expect to see more research exploring similar decompositions of neural rendering tasks. Future work might delve into further refining GTM's approach or extending it to more complex time-variant factors such as physical interactions within a scene.
Conclusion
The Gaussian Time Machine makes a significant stride in bridging the gap between high-quality 3D rendering and real-time performance. By modeling time-variant scenes efficiently, it opens up new frontiers for both practical applications and theoretical advancements in neural rendering.
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