To enhance the temporal coherence of rendered point cloud videos and mitigate jittering artifacts, several strategies can be employed. First, integrating temporal smoothing techniques can help maintain consistency across frames. This could involve using motion vectors to predict the position of points in subsequent frames, allowing for smoother transitions and reducing abrupt changes in point positions.
Additionally, implementing a temporal consistency loss during the training of the neural network could encourage the model to produce outputs that are more stable over time. This loss function could penalize significant changes in the rendered output between consecutive frames, promoting smoother animations.
Another approach is to utilize a frame interpolation technique, where intermediate frames are generated based on the existing frames. This can be achieved through optical flow methods or deep learning-based approaches that predict the in-between states of the point cloud, thus creating a more fluid visual experience.
Lastly, enhancing the model's ability to handle dynamic scenes by incorporating recurrent neural network (RNN) architectures or temporal convolutional networks (TCNs) could allow the system to learn and adapt to temporal patterns in the data, further improving the coherence of the rendered video.