- The paper presents a feed-forward method that leverages pre-trained monocular depth estimation to achieve efficient, robust 3D scene reconstruction.
- It utilizes a novel Gaussian Splatting technique to model both visible and occluded elements, yielding state-of-the-art performance across multiple datasets.
- The approach demonstrates exceptional generalizability and efficiency, training on a single GPU in a day while outperforming models on unseen data.
AnyScene: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image
The paper presents AnyScene, a method for efficient scene reconstruction and novel view synthesis from a single image. The distinguishing feature of AnyScene is its remarkable generalizability and efficiency. The method builds upon an initial monocular depth estimation model, extending it to a more comprehensive 3D shape and appearance reconstructor, leveraging feed-forward Gaussian Splatting for the latter task.
Main Contributions
Generalisability
The authors leverage a pre-trained monocular depth estimation model to serve as the foundational layer of their 3D reconstruction pipeline. This foundation allows AnyScene to generalize effectively across different datasets, as opposed to existing monocular scene reconstruction methods that typically require retraining for new datasets. Notably, the method achieves state-of-the-art results when trained and tested on RealEstate10K, outperforming competitors by a significant margin when transferred to unseen datasets like NYU and KITTI.
Efficiency
AnyScene's efficiency is also a key highlight, demonstrating a training time on a single GPU within a day, making it accessible to the broader research community. The model's efficiency is attributed to the utilization of feed-forward Gaussian Splatting. The approach employs a two-layer Gaussian mixture model to predict the 3D geometry and appearance directly from an image. The first layer handles the visible parts of the scene, while the second layer addresses occlusions and truncations, ensuring a more comprehensive 3D reconstruction.
Detailed Methodology
The core of AnyScene's methodology is twofold:
- Monocular Depth Prediction: Using an off-the-shelf depth prediction model, the depth map generated is highly accurate and serves as the initial estimation for the 3D structure of the scene.
- Gaussian Splatting: The method proceeds by predicting sets of 3D Gaussians for each pixel in the image. These Gaussians represent both visible and occluded parts of the scene. The feed-forward nature of this step ensures computational efficiency. Gaussians are deterministically sampled to represent different layers within the scene, modeling both the nearest surfaces and those occluded or truncated.
Empirical Results
The empirical evaluation of AnyScene is extensive and covers multiple datasets, assessing both cross-domain and in-domain generalization capabilities. On the in-domain task using the RealEstate10K dataset, AnyScene achieves state-of-the-art performance across various metrics including PSNR, SSIM, and LPIPS.
When evaluated on cross-domain datasets such as NYU and KITTI, AnyScene significantly outperforms existing models that were specifically trained on these datasets, demonstrating superior generalization. Specifically, AnyScene achieves better PSNR on KITTI than models trained solely on that dataset. This underscores the effectiveness of leveraging pre-trained depth models and the robustness of the Gaussian Splatting approach.
Theoretical and Practical Implications
Theoretically, AnyScene's approach paves the way for integrating pre-trained depth predictors into broader 3D reconstruction frameworks, highlighting the potential for significant reductions in computational cost and time without sacrificing performance. Additionally, the deterministic Gaussian splatting method introduces a novel way of handling occlusions and scene completions, which could influence future research in neural scene representation and feed-forward reconstruction methods.
Practically, AnyScene's efficiency and generalization capabilities make it a valuable tool for numerous applications, including augmented reality, virtual reality, autonomous driving, and even 3D content creation. The accessibility of this model to researchers with modest computational resources broadens the scope for experimental and applied research in computer vision and 3D reconstruction.
Future Directions
Potential future developments include enhancing the model to handle dynamic scenes or integrating it with other neural rendering techniques to improve temporal consistency in video sequences. Moreover, incorporating more sophisticated priors or other forms of foundational models could further improve the generalization capabilities and robustness of AnyScene.
In conclusion, AnyScene offers a highly efficient and generalizable method for monocular 3D scene reconstruction. By leveraging pre-trained monocular depth predictors and employing a novel Gaussian Splatting approach, it sets a new benchmark in terms of performance, efficiency, and accessibility for monocular scene reconstruction tasks. This paper contributes both to the theoretical landscape and the practical toolkit of contemporary computer vision research.