Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering

• URL: http://arxiv.org/abs/2304.10075v2
• Date: Mon, 18 Sep 2023 10:57:33 GMT
• Title: Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering
• Authors: Dongting Hu, Zhenkai Zhang, Tingbo Hou, Tongliang Liu, Huan Fu and Mingming Gong
• Abstract summary: Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information. We propose mip voxel grids (Mip-VoG), an explicit multiscale representation for real-time anti-aliasing rendering. Our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously.
• Score: 84.37776381343662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rendering scheme in neural radiance field (NeRF) is effective in rendering a pixel by casting a ray into the scene. However, NeRF yields blurred rendering results when the training images are captured at non-uniform scales, and produces aliasing artifacts if the test images are taken in distant views. To address this issue, Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information. Nevertheless, this approach is only suitable for offline rendering since it relies on integrated positional encoding (IPE) to query a multilayer perceptron (MLP). To overcome this limitation, we propose mip voxel grids (Mip-VoG), an explicit multiscale representation with a deferred architecture for real-time anti-aliasing rendering. Our approach includes a density Mip-VoG for scene geometry and a feature Mip-VoG with a small MLP for view-dependent color. Mip-VoG encodes scene scale using the level of detail (LOD) derived from ray differentials and uses quadrilinear interpolation to map a queried 3D location to its features and density from two neighboring downsampled voxel grids. To our knowledge, our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously. We conducted experiments on multiscale datasets, and the results show that our approach outperforms state-of-the-art real-time rendering baselines.

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