3D Data Science with Python

Chapter 16. PointNet for 3D Object Classification

3D scene understanding, a crucial aspect of spatial AI systems, depends heavily on the effective semantic extraction of 3D data. Chapters 12 through 14 have allowed us to leverage both unsupervised and supervised 3D machine learning for this goal, when we have limited labeled datasets. However, when you benefit from large-scale data repositories, 3D deep learning shows promise, which we highlighted in Chapter 15 with 3D CNNs.

However, 3D CNNs are inadequate for handling the complexities of point clouds, which are unstructured datasets without a fixed grid or pixel-based representation. This limitation highlights the critical need for innovative approaches to directly processing and interpreting point cloud data.

Indeed, various methods exist for representing and processing 3D data, such as voxels, meshes, and multiview images (see Chapter 4). However, each of these representations has its drawbacks. While suitable for 3D CNNs, voxels can be computationally intensive and memory-demanding, especially for high-resolution inputs. Meshes or B-reps, processed using GNNs, present challenges in graph construction and computational expense. Multiview CNNs, which leverage 2D CNNs on multiple 2D views, require extensive preprocessing and may not fully capture the inherent 3D structure. These limitations underscore the need for a more efficient and direct approach to 3D data processing (see Chapter 15).

PointNet emerges as a pivotal solution ...