论文标题
Sinerf：联合姿势估计和场景重建的正弦神经辐射场
SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction
论文作者
论文摘要
NERFMM是处理关节优化任务的神经光芒度字段（NERF），即同时重建现实世界场景和注册相机参数。尽管Nerfmm产生了精确的场景综合和姿势估计，但它仍然努力优于挑战场景上的全票基线。在这项工作中，我们确定关节优化中存在系统的次级优化，并进一步确定了多个潜在来源。为了减少潜在来源的影响，我们提出了正弦神经辐射场（SINERF），以利用正弦激活来进行辐射映射和新型混合区域采样（MRS），以有效地选择射线批次。定量和定性结果表明，与NERFMM相比，Sinerf在图像合成质量和姿势估计精度方面取得了全面的显着改善。代码可在https://github.com/yitongx/sinerf上找到。
NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i.e., reconstructing real-world scenes and registering camera parameters simultaneously. Despite NeRFmm producing precise scene synthesis and pose estimations, it still struggles to outperform the full-annotated baseline on challenging scenes. In this work, we identify that there exists a systematic sub-optimality in joint optimization and further identify multiple potential sources for it. To diminish the impacts of potential sources, we propose Sinusoidal Neural Radiance Fields (SiNeRF) that leverage sinusoidal activations for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray batch efficiently. Quantitative and qualitative results show that compared to NeRFmm, SiNeRF achieves comprehensive significant improvements in image synthesis quality and pose estimation accuracy. Codes are available at https://github.com/yitongx/sinerf.
