TiV-NeRF: Tracking and Mapping via Time-Varying Representation with Dynamic Neural Radiance Fields

Previous attempts to integrate Neural Radiance Fields (NeRF) into Simultaneous Localization and Mapping (SLAM) framework either rely on the assumption of static scenes or treat dynamic objects as outliers. However, most of real-world scenarios is dynamic. In this paper, we propose a time-varying representation to track and reconstruct the dynamic scenes. Our system simultaneously maintains two processes, tracking process and mapping process. For tracking process, the entire input images are uniformly sampled and training of the RGB images are self-supervised. For mapping process, we leverage know masks to differentiate dynamic objects and static backgrounds, and we apply distinct sampling strategies for two types of areas. The parameters optimization for both processes are made up by two stages, the first stage associates time with 3D positions to convert the deformation field to the canonical field. And the second associates time with 3D positions in canonical field to obtain colors and Signed Distance Function (SDF). Besides, We propose a novel keyframe selection strategy based on the overlapping rate. We evaluate our approach on two publicly available synthetic datasets and validate that our method is more effective compared to current state-of-the-art dynamic mapping methods.