X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360<sup>°</sup> Insufficient RGB-D Views

Zhu, Haoyi

doi:10.1109/wacv56688.2023.00572

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…proposed NeRF-Loc, a new visual relocalization method based on directly matching implicit 3D descriptors and 2D images. The X-NeRF proposed by Zhu, HY et al [36] can train a method that can represent multiple scenes and 360°views and models with insufficient RGB-D images. The VT-NeRF prposed by Hao, FY et al [37] can learn the neural radiation field of the dynamic human body.…”

Section: Nerf and Nerf Variantsmentioning

confidence: 99%

STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

Ma,

Liu,

Sun

et al. 2024

Remote Sensing

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Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF’s performance cannot sufficiently meet the project’s needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network’s input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model’s overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures.

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Section: Nerf and Nerf Variantsmentioning

confidence: 99%

STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

Ma,

Liu,

Sun

et al. 2024

Remote Sensing

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show abstract

“…Different from classical matrix-based discrete representation, coordinate networks focus on learning a neural mapping function with low-dimensional coordinates inputs and the corresponding signal values outputs, and have demonstrated the advantages of continuous querying and memory-efficient footprint in various signal representation tasks, such as images [5], [6], [7], scenes [24], [27], [30], [35] and videos [21], [22]. Additionally, coordinate networks could be seamlessly combined with different differentiable physical processes, opening a new way for solving various inverse problems, especially the domain-specific tasks where large-scale labelled datasets are unavailable, such as the shape representation [23], [25], [28], [29], [36], computed tomography reconstruction [26], [31], [32], [33], [34] and inverse rendering for novel view synthesis [37], [38], [41], [42], [100].…”

Section: Coordinate Networkmentioning

confidence: 99%

A Multi-Scale Similarity Based Community Detection for Attributed Complex Networks

Cai

Hao

Yang

et al. 2023

Preprint

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WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights

Jang,

Lee,

Jang

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360^° Insufficient RGB-D Views

Cited by 4 publications

References 45 publications

STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

A Multi-Scale Similarity Based Community Detection for Attributed Complex Networks

WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights

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X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360° Insufficient RGB-D Views

Cited by 4 publications

References 45 publications

STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

A Multi-Scale Similarity Based Community Detection for Attributed Complex Networks

WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights

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Product

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X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360^° Insufficient RGB-D Views