G3R: Gradient Guided Generalizable Reconstruction (2409.19405v1)

Abstract: Large scale 3D scene reconstruction is important for applications such as virtual reality and simulation. Existing neural rendering approaches (e.g., NeRF, 3DGS) have achieved realistic reconstructions on large scenes, but optimize per scene, which is expensive and slow, and exhibit noticeable artifacts under large view changes due to overfitting. Generalizable approaches or large reconstruction models are fast, but primarily work for small scenes/objects and often produce lower quality rendering results. In this work, we introduce G3R, a generalizable reconstruction approach that can efficiently predict high-quality 3D scene representations for large scenes. We propose to learn a reconstruction network that takes the gradient feedback signals from differentiable rendering to iteratively update a 3D scene representation, combining the benefits of high photorealism from per-scene optimization with data-driven priors from fast feed-forward prediction methods. Experiments on urban-driving and drone datasets show that G3R generalizes across diverse large scenes and accelerates the reconstruction process by at least 10x while achieving comparable or better realism compared to 3DGS, and also being more robust to large view changes.

• The paper presents an innovative 3D Neural Gaussian representation that integrates explicit geometry with learned data-driven priors, enabling rapid scene reconstruction.
• It employs an iterative gradient feedback mechanism using differentiable rendering to efficiently handle occlusions and aggregate multi-view data.
• G3R-Net, the reconstruction network, achieves robust performance with significantly fewer iterations and high photorealism across dynamic, large-scale environments.

Gradient Guided Generalizable Reconstruction (G3R)

The paper "G3R: Gradient Guided Generalizable Reconstruction" introduces an innovative approach aimed at enhancing the efficiency and quality of large-scale 3D scene reconstruction. Currently, neural rendering techniques such as NeRF and 3D Gaussian Splatting (3DGS) offer high-fidelity outputs but suffer from slow per-scene optimization and artifacts when there are significant viewpoint changes. Generalizable models like MVSNeRF, ENeRF, and GNT, while faster, typically produce lower-quality renderings and are confined to smaller environments. The proposed Gradient Guided Generalizable Reconstruction (G3R) method effectively integrates the high photorealism of per-scene optimizations with the speed and scalability of data-driven predictions.

Key Contributions

1. Unified 3D Representation (3D Neural Gaussians): G3R introduces an advanced scene representation known as 3D Neural Gaussians, which combines the attributes of 3D Gaussians with latent feature vectors. This augmented representation not only encapsulates explicit geometric details but also harnesses the power of learned data-driven priors, thereby enabling fast and robust large-scale scene reconstruction.
2. Iterative Gradient Feedback: Unlike traditional methods that independently process 2D images to lift them into 3D space, G3R employs a novel technique of accumulating gradients via differentiable rendering. This gradient-based approach intrinsically handles occlusions and efficiently aggregates visual data from numerous source images, leading to a coherent and comprehensive 3D scene model.
3. Learned Optimization for Reconstruction: At the core of G3R is a reconstruction network, G3R-Net, which iteratively refines the 3D representation. This network learns to predict updates based on both the current 3D state and the gradient signals derived from rendering discrepancies. Such a procedure leverages spatial correlations and data-driven priors effectively, accelerating convergence (24 iterations versus 1000s in traditional methods) and enhancing robustness across varying viewpoints.
4. Scalable Scene Decomposition: To handle dynamic entities and vast unbounded areas, the paper proposes decomposing scenes into static backgrounds, dynamic actors, and distant terrain. Each component is represented and processed through tailored 3D Neural Gaussian sets, ensuring scalability and detailed modeling of complex real-world environments.

Experimental Validation

The performance and robustness of G3R are empirically validated on two substantial public datasets: PandaSet and BlendedMVS. The paper reports:

• Quantitative Metrics:

On PandaSet, G3R achieves high PSNR (25.22), SSIM (0.742), and low LPIPS (0.371) with a reconstruction time of just 123 seconds, making it comparable or superior to both generalizable methods (e.g., ENeRF, GNT) and per-scene optimization techniques (e.g., 3DGS). Similarly, G3R exhibits strong performance on BlendedMVS with significant improvements over baseline methods in terms of PSNR and visual realism.

• Visual Comparisons:

Qualitative results underscore G3R's ability to produce clear, sharp, and artifact-free renderings even in challenging scenarios involving large viewpoint changes and dynamic elements. Traditional generalizable models, by contrast, display blurring and discontinuities, whereas per-scene methods often overfit to the input views.

Implications and Future Work

The practical implications of G3R are multi-faceted:

1. Real-Time High-Fidelity VR/AR: By significantly reducing reconstruction times while maintaining high photorealism, G3R is well-suited for virtual reality and augmented reality applications, where real-time rendering and interaction are crucial.
2. Autonomous Navigation Simulation: G3R's capability to generate realistic and editable 3D scenes can benefit the simulation environments for autonomous vehicle training and testing, offering scalable and safe avenues to evaluate various driving scenarios.
3. Cross-Dataset Generalization: The experiments also reveal G3R's potential for cross-dataset generalization. A model trained on PandaSet demonstrates robust performance on the Waymo Open Dataset with minimal fine-tuning, indicating versatility across different domains and sensor configurations.

Conclusion

G3R represents a substantial advancement in neural rendering techniques, effectively bridging the trade-off between accuracy, speed, and scalability in large-scale 3D scene reconstruction. The proposed method enhances robustness against viewpoint changes, accelerates reconstruction processes, and offers a unified, editable representation suitable for a variety of applications, from virtual simulations to real-world robotic systems. Future research may explore incorporating advanced surface regularization, adversarial training, and handling sparse initializations, further extending G3R’s capabilities and applications.