The current method exhibits limitations when dealing with severe point cloud noise or misalignment, primarily due to its reliance on the quality of the input data. The neural network, while robust to moderate noise and quantization, may struggle with significant noise levels that obscure the underlying geometry of the scene. This can lead to inaccurate Gaussian parameter estimations, resulting in poor rendering quality.
To address these limitations, several strategies can be implemented. First, incorporating advanced denoising techniques prior to the rendering process can help clean the point cloud data. Techniques such as bilateral filtering, non-local means, or deep learning-based denoising algorithms can effectively reduce noise while preserving important geometric features.
Additionally, augmenting the training dataset with examples of noisy and misaligned point clouds can improve the model's robustness. By exposing the neural network to a wider variety of conditions during training, it can learn to better handle such scenarios in real-world applications.
Furthermore, implementing a multi-stage processing pipeline that includes a preliminary alignment step could help correct misalignments before rendering. Techniques such as Iterative Closest Point (ICP) or deep learning-based alignment methods can be employed to ensure that the point clouds are accurately positioned relative to one another.