论文标题
GARF：高保真重建和姿势估计的高斯活化的辐射场
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
论文作者
论文摘要
尽管神经辐射场（NERF）显示出令人信服的结果，这些结果是对现实世界场景的新颖观点的综合，但大多数现有方法都需要准确的先前摄像头姿势。尽管存在共同恢复辐射场和相机姿势的方法（BARF），但它们依赖于繁琐的粗到细节辅助位置嵌入以确保良好的性能。我们提出了高斯激活的神经辐射场（GARF），这是一种新的无位置嵌入神经辐射域架构 - 采用高斯激活 - 在高富裕性重建和姿势估计方面，其表现优于当前最新的。
Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses. Although approaches for jointly recovering the radiance field and camera pose exist (BARF), they rely on a cumbersome coarse-to-fine auxiliary positional embedding to ensure good performance. We present Gaussian Activated neural Radiance Fields (GARF), a new positional embedding-free neural radiance field architecture - employing Gaussian activations - that outperforms the current state-of-the-art in terms of high fidelity reconstruction and pose estimation.
