We propose a graph-based neural network architecture that, given a point cloud sequence, can make accurate predictions of future frames.
We develop the following contributions:
We developed a technique for feature disentanglement and feature visualization.
Using this technique, we explained hierarchical features in the context of dynamic point cloud processing. We have shown
We investigated denoising point cloud generated by millimeter-wave (mmWave) radar sensor.
Millimeter-wave sensors produce point clouds that are much sparser and noisier than other point cloud data (e.g., LiDAR), yet they are more robust in challenging conditions such as in the presence of fog, dust, smoke, or rain. Our goal is denoising this data
Our contributions are the following
Light Field images are a rich representation of the 3D scene. Due to their large size, Light Field images require efficient compression algorithms.
We proposed an efficient, scalable light field image coding method.
Our methods create a ‘pseudo-video’ sequence from the image using a novel Quadratic Scan Order of our design. The video is then encoded using (HEVC) encoders. Besides superior compression results, our methods has improved random access functionalities.
Parasite-host dynamics can be modeled using graph-representations.