HG3-NeRF: Hierarchical Geometric, Semantic, and Photometric Guided Neural Radiance Fields for Sparse View Inputs
(2401.11711)Abstract
Neural Radiance Fields (NeRF) have garnered considerable attention as a paradigm for novel view synthesis by learning scene representations from discrete observations. Nevertheless, NeRF exhibit pronounced performance degradation when confronted with sparse view inputs, consequently curtailing its further applicability. In this work, we introduce Hierarchical Geometric, Semantic, and Photometric Guided NeRF (HG3-NeRF), a novel methodology that can address the aforementioned limitation and enhance consistency of geometry, semantic content, and appearance across different views. We propose Hierarchical Geometric Guidance (HGG) to incorporate the attachment of Structure from Motion (SfM), namely sparse depth prior, into the scene representations. Different from direct depth supervision, HGG samples volume points from local-to-global geometric regions, mitigating the misalignment caused by inherent bias in the depth prior. Furthermore, we draw inspiration from notable variations in semantic consistency observed across images of different resolutions and propose Hierarchical Semantic Guidance (HSG) to learn the coarse-to-fine semantic content, which corresponds to the coarse-to-fine scene representations. Experimental results demonstrate that HG3-NeRF can outperform other state-of-the-art methods on different standard benchmarks and achieve high-fidelity synthesis results for sparse view inputs.
Overview
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HG³-NeRF is an enhancement of NeRF that offers improved photorealistic image synthesis from sparse view inputs through hierarchical guidance techniques.
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It sidesteps the limitations of previous NVS methods by employing hierarchical geometric and semantic guidance mechanisms without depending on dense input data.
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Hierarchical Geometric Guidance (HGG) uses depth priors locally to guide volume sampling and avoid geometric misalignment in scene representation.
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Hierarachical Semantic Guidance (HSG) improves semantic consistency by progressively integrating features from different resolution levels during training.
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Experimental results show that HG³-NeRF outperforms other methodologies, providing high-quality results in sparse input scenarios without the need for normalized device coordinate space.
Introduction
Novel View Synthesis (NVS) is essential for creating photorealistic images from new perspectives not originally captured by the input views. Neural Radiance Fields (NeRF) have emerged as a state-of-the-art framework for this task, providing impressive results by learning continuous scene representations. Despite the success, NeRF's dependence on densely sampled views for reliable performance limits its practicality in real-world applications where data acquisition is constrained. The essence of the Hierarchical Geometric, Semantic, and Photometric Guided NeRF (HG³-NeRF) technique lies in its ability to effectively utilize sparse view inputs, alleviating NeRF’s limitation and enhancing view synthesis quality through innovative hierarchical guidance strategies.
Related Works and Motivation
Earlier methodologies for addressing NVS from sparse views can be broadly categorized into pre-training methods that leverage large datasets to train a model before fine-tuning on target scenes, and per-scene optimization methods that optimize the model from scratch for each scenario. Both strategies exhibit limitations, such as dependency on dataset quality or a lack of geometric supervision, resulting in geometric misalignment. The HG³-NeRF approach sidesteps these concerns by introducing a novel hierarchical geometric guidance mechanism (HGG) and hierarchical semantic guidance (HSG), utilizing sparse depth priors and semantic content learning for consistent scene representation across varied resolutions.
Hierarchical Geometric and Semantic Guidance
HGG and HSG are the fundament of HG³-NeRF's robustness against input view sparsity. Inspired by the potential bias from direct depth supervision, HGG employs a local-to-global volume sampling strategy that uses depth priors as guidance rather than as an exact constraint, thereby circumventing geometric misalignment. HSG addresses the challenge posed by semantic consistency across images with different resolutions. The method initially supervises using features from down-sampled images, which match the blurred, low-frequency content of images generated early in training. As the training progresses and the images gain detail, HSG incrementally incorporates finer features.
Experimental Results
The HG³-NeRF model was rigorously tested against standard benchmarks, outperforming other state-of-the-art techniques. Notably, by employing HGG, the model showcased the capability to refine scene representations under sparse input conditions significantly. It enabled realistic synthesis results that maintained geometric consistency without succumbing to misalignments introduced by depth priors. The integration of HSG enhanced semantic consistency across reconstructions, adding to the model's robustness. The combination of HGG and HSG allowed the model to sidestep the use of the Normalized Device Coordinate (NDC) space, traditionally utilized in NVS tasks, and operate effectively in real-world space for forward-facing scenarios.
Conclusion and Future Directions
HG³-NeRF marks a noteworthy progression in the field of NVS, particularly for scenarios constrained by sparse input views. The advent of hierarchical geometric and semantic strategies unlocks new possibilities, mitigating traditional reliance on dense input data and intricate pre-processing stages. Despite these advances, the requirement for accurately estimated camera poses remains a challenge and highlights a proximate area for future exploration — refining NeRF optimization capabilities further when confronted with noisy camera poses and limited input data.
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