Re-Nerfing: Improving Novel Views Synthesis through Novel Views Synthesis
(2312.02255)Abstract
Neural Radiance Fields (NeRFs) have shown remarkable novel view synthesis capabilities even in large-scale, unbounded scenes, albeit requiring hundreds of views or introducing artifacts in sparser settings. Their optimization suffers from shape-radiance ambiguities wherever only a small visual overlap is available. This leads to erroneous scene geometry and artifacts. In this paper, we propose Re-Nerfing, a simple and general multi-stage data augmentation approach that leverages NeRF's own view synthesis ability to address these limitations. With Re-Nerfing, we enhance the geometric consistency of novel views as follows: First, we train a NeRF with the available views. Then, we use the optimized NeRF to synthesize pseudo-views around the original ones with a view selection strategy to improve coverage and preserve view quality. Finally, we train a second NeRF with both the original images and the pseudo views masking out uncertain regions. Extensive experiments applying Re-Nerfing on various pipelines on the mip-NeRF 360 dataset, including Gaussian Splatting, provide valuable insights into the improvements achievable without external data or supervision, on denser and sparser input scenarios. Project page: https://renerfing.github.io
Overview
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Re-Nerfing is a new technique to enhance NeRF models, especially with limited data, by enforcing geometric consistency and improving novel view synthesis.
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The process starts with the traditional NeRF training, followed by generating synthetic views to train a second NeRF model with additional geometric constraints.
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The enforcement of epipolar geometry constraints during retraining leads to more accurate depth estimation and scene representation.
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Re-Nerfing provides significant improvements in dense and sparse scenarios, refining novel views and mitigating insufficient data issues.
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The methodology introduces a novel density loss function and doesn't require extra data or models, but also relies on the quality of the base NeRF model's outputs.
Enhancing 3D Scene Reconstruction with Re-Nerfing
Creating stunningly realistic three-dimensional scenes from a collection of images is a popular application of AI technology. Specifically, Neural Radiance Fields (NeRFs) have revolutionized this field by synthesizing new perspectives on a scene that weren't captured in the original dataset. Nonetheless, this technology is not without its challenges – when limited data is available, artifacts and inaccuracies tend to creep into the 3D representations. Addressing these limitations, a recent development presents an ingenious approach: Re-Nerfing.
Re-Nerfing is a novel, multi-stage technique designed to enhance the output of NeRF models, particularly when they are fed sparser datasets. It takes great advantage of NeRF's inherent ability to synthesize views, building upon the original model to enforce geometric consistency and improve the quality of novel views. At its core, Re-Nerfing initially sticks to the standard procedure of training a NeRF model with available views. Following that, it generates additional pseudo-views that simulate a stereo or trifocal camera setup and retrains a second NeRF model using both the original and artificially generated images. During this retraining, the system integrates additional geometric constraints, pushing the scene's representation towards greater fidelity.
The key to Re-Nerfing's success is its enforcement of epipolar geometry constraints from synthetic views. These constraints guide the estimation of depth and density during the second round of NeRF model training, resulting in more accurate and geometrically-consistent synthetic views. Extensive experiments demonstrate that harnessing these synthetic views to retrain the model (Re-Nerfing) leads to noticeable enhancements even when the input scenarios are already dense.
Interestingly, results from Re-Nerfing improve along two axes. First, when training data is dense, Re-Nerfing refines novel views, particularly those with lower visibility in the training dataset. For less dense training scenarios, the benefits are even more significant, suggesting that the technique efficaciously mitigates issues arising from insufficient data.
Re-Nerfing's methodology doesn't just stop at enhancing scene fidelity; it also offers a novel density loss derived from epipolar geometry. This aspect is portable and can potentially boost any stereo-setup used in training NeRF models. Furthermore, Re-Nerfing doesn't lean on extraneous data or models. It generates and utilizes synthetic views solely based on the images already available to the baseline NeRF model, preserving the advantages of the original technology.
In practical terms, the Re-Nerfing approach holds promise for those looking to create detailed 3D models from limited visual data. It could transform applications wherein resources to capture comprehensive datasets are scarce, such as archaeological documentation and virtual reality content creation.
In terms of limitations, the current iteration of Re-Nerfing hinges on the quality of the base NeRF model's renderings – if the first-stage model doesn't generate reasonable scene geometry, the benefits of Re-Nerfing diminish. Also, the method isn't as potent in extremely sparse scenes, though blending it with other strategies targeting these scenarios could be a research path worth exploring. Additionally, the simple patch matching for enforcing geometric constraints might encounter issues in featureless or repetitive regions. Bringing in advanced feature matching strategies could help strengthen the technique further.
In conclusion, Re-Nerfing epitomizes an astute exploitation of NeRF's synthesis capabilities, guiding the technology to new heights of precision and efficiency. By turning NeRF's weaknesses into strengths, Re-Nerfing paves the way for more robust and detailed 3D scene reconstructions.
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