Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis
نویسندگان
چکیده
Recently, Neural Radiance Fields (NeRF) is revolutionizing the task of novel view synthesis (NVS) for its superior performance. In this paper, we propose to synthesize dynamic scenes. Extending methods static scenes not straightforward as both scene geometry and appearance change over time, especially under monocular setup. Also, existing NeRF generally require a lengthy per-scene training procedure, where multi-layer perceptrons (MLP) are fitted model motions radiance. built on top recent advances in voxel-grid optimization, fast deformable radiance field method handle Our consists two modules. The first module adopts deformation grid store 3D features, light-weight MLP decoding that maps point observation space canonical using interpolated features. second contains density color scene. occlusion explicitly modeled further improve rendering quality. Experimental results show our achieves comparable performance D-NeRF only 20 minutes training, which more than 70 $$\times $$ faster D-NeRF, clearly demonstrating efficiency proposed method.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2023
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-26319-4_27