PFGS: High Fidelity Point Cloud Rendering via Feature Splatting
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917713376444416 |
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| author | Wang, Jiaxu Zhang, Ziyi He, Junhao Xu, Renjing |
| author_facet | Wang, Jiaxu Zhang, Ziyi He, Junhao Xu, Renjing |
| contents | Rendering high-fidelity images from sparse point clouds is still challenging. Existing learning-based approaches suffer from either hole artifacts, missing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estimate Gaussian properties in a point-wise manner, the estimated properties are used to rasterize neural feature descriptors into 2D planes which are extracted from a multiscale extractor. The projected feature volume is gradually decoded toward the final prediction via a multiscale and progressive decoder. The whole pipeline experiences a two-stage training and is driven by our well-designed progressive and multiscale reconstruction loss. Experiments on different benchmarks show the superiority of our method in terms of rendering qualities and the necessities of our main components. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PFGS: High Fidelity Point Cloud Rendering via Feature Splatting Wang, Jiaxu Zhang, Ziyi He, Junhao Xu, Renjing Computer Vision and Pattern Recognition Rendering high-fidelity images from sparse point clouds is still challenging. Existing learning-based approaches suffer from either hole artifacts, missing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estimate Gaussian properties in a point-wise manner, the estimated properties are used to rasterize neural feature descriptors into 2D planes which are extracted from a multiscale extractor. The projected feature volume is gradually decoded toward the final prediction via a multiscale and progressive decoder. The whole pipeline experiences a two-stage training and is driven by our well-designed progressive and multiscale reconstruction loss. Experiments on different benchmarks show the superiority of our method in terms of rendering qualities and the necessities of our main components. |
| title | PFGS: High Fidelity Point Cloud Rendering via Feature Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.03857 |