ProSGNeRF: Progressive Dynamic Neural Scene Graph with Frequency Modulated Auto-Encoder in Urban Scenes
(2312.09076)Abstract
Implicit neural representation has demonstrated promising results in view synthesis for large and complex scenes. However, existing approaches either fail to capture the fast-moving objects or need to build the scene graph without camera ego-motions, leading to low-quality synthesized views of the scene. We aim to jointly solve the view synthesis problem of large-scale urban scenes and fast-moving vehicles, which is more practical and challenging. To this end, we first leverage a graph structure to learn the local scene representations of dynamic objects and the background. Then, we design a progressive scheme that dynamically allocates a new local scene graph trained with frames within a temporal window, allowing us to scale up the representation to an arbitrarily large scene. Besides, the training views of urban scenes are relatively sparse, which leads to a significant decline in reconstruction accuracy for dynamic objects. Therefore, we design a frequency auto-encoder network to encode the latent code and regularize the frequency range of objects, which can enhance the representation of dynamic objects and address the issue of sparse image inputs. Additionally, we employ lidar point projection to maintain geometry consistency in large-scale urban scenes. Experimental results demonstrate that our method achieves state-of-the-art view synthesis accuracy, object manipulation, and scene roaming ability. The code will be open-sourced upon paper acceptance.
Overview
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Introduces a novel neural rendering approach called ProSGNeRF for reconstructing dynamic urban scenes.
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ProSGNeRF uses a progressive neural scene graph to handle dynamic objects and maintain global consistency.
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Frequency modulation in the auto-encoder compensates for sparse view inputs and improves viewpoint consistency.
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Geometry consistency is ensured through Lidar point projection, enhancing scalability for large scenes.
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Experimental results on KITTI and VKITTI datasets show ProSGNeRF outperforms contemporary methods.
Introduction
The field of computer vision has seen rapid advancements in rendering realistic three-dimensional scenes from two-dimensional images. Applications like augmented reality, virtual reality, and autonomous driving stand to benefit significantly from methods that can synthesize views of a scene from different angles, even with limited photographic data. Here, we will explore a method for reconstructing large-scale dynamic urban scenes which contain numerous moving elements.
Core Challenges
Urban scenes present unique challenges for neural rendering techniques:
- Handling Dynamic Elements and Camera Movement: Urban scenes often contain multiple objects in motion. Combined with the movement of the camera (referred to as "camera ego-motion"), this complexity can reduce the quality of synthesized views.
- Sparse View Input: Urban scenes frequently lack multiple viewpoints for the same object, undermining reconstruction performance.
- Annotation and Labeling Limitations: Obtaining accurate annotations and labels for real-world scenes is an arduous task.
In response to these challenges, this paper introduces a novel approach called ProSGNeRF (Progressive Dynamic Neural Scene Graph with Frequency Modulated Auto-Encoder) for reconstructing and rendering large-scale urban scenes.
Methodology
Progressive Neural Scene Graph
ProSGNeRF structures the environment into a graph representation, integrating local scene representations and background segmentation. The neural scene graph arranges the scene into dynamic objects and static backgrounds progressively, elegantly scaling to represent larger scenes. As the estimated camera trajectory evolves, the system cleverly initializes local frame subsets, progressively expanding the scene representation while maintaining global consistency.
Frequency Modulated Auto-Encoder
The proposed auto-encoder addresses the sparsity of view inputs through frequency modulation. By encoding shape and appearance characteristics, the auto-encoder compensates for the limited observational data on dynamic objects. This technique helps prevent overfitting and improves consistency across different viewpoints.
Geometry Consistency and Scalability
The method also introduces a technique to maintain geometry consistency using Lidar point projection in large urban scenes, a significant addition for scenarios involving extensive camera motion. Scalability is accomplished through this progressive neural graph approach, dynamically allocating new local graphs based on temporal windows.
Experimental Results
Evaluations conducted on the KITTI and VKITTI datasets validate the ProSGNeRF's effectiveness, outperforming contemporary methods across standard metrics like PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index Measure), and LPIPS (Learned Perceptual Image Patch Similarity). The approach excels not only in image reconstruction and view synthesis but also in scene editing, demonstrating the capability to manipulate and adjust dynamic objects within the scene.
Conclusion
The ProSGNeRF method presents a significant contribution to the neural rendering field, notably succeeding where prior methods struggled: in representating dynamic objects amid extensive camera movement across large urban spaces. The authors have emphasized practical applications such as virtual reality fly-throughs and autonomous driving simulators, which could leverage these techniques for enhanced realism and efficiency. The openness to handle arbitrary scene scales, coupled with frequency modulation for better dynamic object representation, set ProSGNeRF as a pioneering technique suitable for complex, real-world applications.
Limitations and Future Work
The method comes with caveats such as the need for ground truth camera pose, which is not always available or accurate in real-world settings. Nonetheless, future work could build upon this foundation, potentially exploring unsupervised or semi-supervised strategies that require less precise annotations or no annotations at all.
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