Our approach SPARF produces realistic novel-view rendering given as few as 2 or 3 input images, with noisy camera poses. We add two novel constraints into the pose-NeRF optimization: the multi-view correspondence loss and the depth-consistency loss. Please contact Prune Truong ([email protected]) if you have any questions!

2022年11月21日 · In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses.

We provide PyTorch code for all experiments: BARF/SPARF for joint pose-NeRF training, NeRF/SPARF when considering fixed ground-truth poses as input.

SPARF is a large-scale sparse radiance field dataset consisting of ~ 1 million SRFs with multiple voxel resolutions (32, 128, and 512) and 17 million posed images with a resolution of 400 X 400.

In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses.

2022年12月18日 · In order to leverage machine learning and adoption of SRFs as a 3D representation, we present SPARF, a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of $\sim$ 17 million images rendered from nearly 40,000 shapes at high resolution (400 X 400 pixels).

In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses.

2023年10月8日 · 在这项工作中，我们引入了稀疏姿态调整辐射场（SPARF），以解决只有少数宽基线输入图像（最低仅为3张）和嘈杂的相机姿态的新视图合成挑战。 我们的方法利用多视角几何约束，以共同学习NeRF并优化相机姿态。 通过依赖于提取自输入视图之间的像素匹配，我们的多视图对应目标迫使优化的场景和相机姿态收敛到全局且几何精确的解决方案。 项目主页：http://prunetruong.com/sparf.github.io/ 代码地址;https://github.com/google-research/sparf 论 …

SPARF is a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of ~17 million images rendered from nearly 40,000 shapes at high resolution (400×400 pixels). Source: SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Images

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