Abstract
Recent advances in implicit scene representation enable high-fidelity street view novel view synthesis. However, existing methods optimize a neural radiance field for each scene, relying heavily on dense training images and extensive computation resources. To mitigate this shortcoming, we introduce a new method called Efficient Depth-Guided Urban View Synthesis (EDUS) for fast feed-forward inference and efficient per-scene fine-tuning. Different from prior generalizable methods that infer geometry based on feature matching, EDUS leverages noisy predicted geometric priors as guidance to enable generalizable urban view synthesis from sparse input images. The geometric priors allow us to apply our generalizable model directly in the 3D space, gaining robustness across various sparsity levels. Through comprehensive experiments on the KITTI-360 and Waymo datasets, we demonstrate promising generalization abilities on novel street scenes. Moreover, our results indicate that EDUS achieves state-of-the-art performance in sparse view settings when combined with fast test-time optimization.
Overview
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The paper proposes Efficient Depth-Guided Urban View Synthesis (EDUS), a method designed to improve novel view synthesis (NVS) performance in urban street scenes, particularly for autonomous vehicles that capture sparse images.
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EDUS leverages noisy geometric priors and depth maps from monocular or stereo sources to construct a composite point cloud, which is further refined using a modulated 3D CNN and 2D features to enhance rendering quality.
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The method demonstrates superior performance on KITTI-360 and Waymo datasets in sparse image scenarios, highlighting its robustness and efficiency, and aims to pave the way for future advancements including dynamic object incorporation and real-time processing enhancements.
Efficient Depth-Guided Urban View Synthesis
The recent advancements in neural radiance fields (NeRFs) have revolutionized the task of novel view synthesis (NVS), particularly for urban street scenes. However, common NeRF-based techniques generally require dense image datasets and extensive computational resources for each specific scene, making them less applicable to scenarios typical for autonomous vehicles where sparse images are captured. To address this limitation, the paper introduces a novel method called Efficient Depth-Guided Urban View Synthesis (EDUS). The new approach leverages geometric priors to enhance NVS performance, focusing on robustness and efficiency.
Key Challenges and Proposed Solutions
Sparse View Synthesis in Urban Environments
The core challenge in current NVS techniques, especially for autonomous driving applications, arises from the sparsity of input images. As vehicles usually capture scenes from very few viewpoints while in motion, existing methods like feature matching fall short due to reduced overlap and small parallax angles between images, leading to poor geometry predictions and reconstruction uncertainty.
To mitigate these issues, EDUS uses noisy geometric priors as guidance, ensuring robustness across varying levels of image sparsity. By exploiting predicted monocular or stereo depth maps, the method efficiently generates coherent urban scenes even from a sparse set of images.
Methodology
Depth Estimation and Point Cloud Generation
The approach begins with depth estimation for each input image using either stereo or monocular depth detectors. The resulting depth maps are then unprojected into 3D space to form a composite point cloud. This point cloud serves as an initial geometric scaffold for further refinement.
Leveraging 3D and 2D Features
EDUS employs a modulated 3D Convolutional Neural Network (CNN), specifically a Spatially-Adaptive Normalization (SPADE) network, to process the accumulated point cloud data into a feature volume. The SPADE CNN uses multi-resolution modulation to preserve appearance information and improve generalization. Complementarily, the approach retrieves 2D features from nearby input views using image-based rendering techniques. This dual-feature strategy captures high-frequency details which are typically lost with a purely 3D approach.
Scene Decomposition and Rendering
To represent the unbounded nature of street scenes, the method decomposes the scene into three segments: foreground, background, and sky. The foreground is modeled using the 3D feature volume and 2D image features, while the background and sky rely primarily on image-based rendering techniques. This segmentation enhances the rendering fidelity and mitigates artifacts due to incorrect geometry in the far regions.
Training and Test-Time Optimization
EDUS is trained on datasets containing multiple street scenes with supervision from RGB images and optional LiDAR data for geometrical regularization. The training employs several loss functions including a photometric loss, sky segmentation loss, and entropy regularization loss to promote opaque rendering. For test-time optimization, the method fine-tunes the global feature volume to quickly adapt to novel scenes, which is computationally efficient due to the good initialization provided by the generalizable model.
Results and Comparisons
The method was evaluated on the KITTI-360 and Waymo datasets against state-of-the-art NVS techniques. The results demonstrated that EDUS outperforms existing methods in various sparsity settings, achieving state-of-the-art performance metrics. For instance, in sparsity settings with 50% and 80% of image drops, EDUS surpassed methods like IBRNet and MVSNeRF both in terms of PSNR and SSIM scores.
Moreover, the paper highlights the efficiency of EDUS in fine-tuning, reducing convergence time significantly while maintaining high visual quality. Notably, the point-based methods showed resilience against changes in scene sparsity levels and various camera baselines, emphasizing EDUS’s robustness and applicability to real-world scenarios such as autonomous driving.
Future Directions
The implications of this research are profound for both practical applications in autonomous driving and theoretical advancements in NVS. Future work can aim to incorporate dynamic objects into the synthesis process, an area that remains challenging due to the complex motion patterns and occlusions. Additionally, further investigation into enhancing real-time processing capabilities will be crucial for practical deployment in real-world autonomous systems.
In summary, Efficient Depth-Guided Urban View Synthesis presents a robust, generalizable, and efficient approach to NVS in urban environments, addressing key limitations of current methodologies and paving the way for future advancements in the field.
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