Point Embeddings

Point embeddings represent 3D point cloud data as vectors, enabling efficient processing by machine learning models for tasks like object recognition, segmentation, and registration. Current research focuses on improving the robustness and efficiency of these embeddings, exploring various architectures including multi-layer perceptrons (MLPs), positional embeddings (e.g., random Fourier features), and cross-point embeddings, with a particular emphasis on handling noise, outliers, and partial data. These advancements are crucial for improving the accuracy and scalability of 3D scene understanding in applications such as autonomous driving, robotics, and remote sensing.