Camera pose estimation is a fundamental problem of Augmented Reality and 3D reconstruction systems. Recently, despite the new better performing direct methods being developed, state-of-the-art methods are still estimating erroneous poses due to sensor noise, environmental conditions and challenging trajectories. Adding a back-end mapping process, SLAM systems achieve better performance and are more robust, but require higher computational resources, limiting their applicability. Therefore, lighter solutions to improve the accuracy of pose estimates are required. In this work we demonstrate the effectiveness of lighter data structures, namely surface elements, and exploit the temporality of sensor data streams to accumulate moving camera frames and improve tracking. This representation allows us to ”splat” a photometric and geometric model simultaneously and use it to improve the performance of dense RGB-D camera pose estimation methods. Exploiting Elliptical Weighted Average splatting to produce high quality photometric results also allows us to detect erroneous poses through a novel visual quality analysis process. We show evidence of the EWA temporal model’s effectiveness in publicly available datasets and argue that point-based representations are a good candidate for building lighter systems that should be further explored.