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Previous attempts to integrate Neural Radiance Fields (NeRF) intoSimultaneous Localization and Mapping (SLAM) framework either rely on theassumption of static scenes or treat dynamic objects as outliers. However, mostof real-world scenarios is dynamic. In this paper, we propose a time-varyingrepresentation to track and reconstruct the dynamic scenes. Our systemsimultaneously maintains two processes, tracking process and mapping process.For tracking process, the entire input images are uniformly sampled andtraining of the RGB images are self-supervised. For mapping process, weleverage know masks to differentiate dynamic objects and static backgrounds,and we apply distinct sampling strategies for two types of areas. Theparameters optimization for both processes are made up by two stages, the firststage associates time with 3D positions to convert the deformation field to thecanonical field. And the second associates time with 3D positions in canonicalfield to obtain colors and Signed Distance Function (SDF). Besides, We proposea novel keyframe selection strategy based on the overlapping rate. We evaluateour approach on two publicly available synthetic datasets and validate that ourmethod is more effective compared to current state-of-the-art dynamic mappingmethods.

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