PFGS: High Fidelity Point Cloud Rendering via Feature Splatting
<|reference_start|>PFGS: High Fidelity Point Cloud Rendering via Feature Splatting: 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.<|reference_end|>
@article{wang2024pfgs:,
    title={PFGS: High Fidelity Point Cloud Rendering via Feature Splatting},
    author={Jiaxu Wang and Ziyi Zhang and Junhao He and Renjing Xu},
    journal={arXiv preprint arXiv:2407.03857},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03857},
    primaryClass={cs.CV}
}