Machine Learning Meets Geometry

CSE291 (8) - Winter 2022

Lecture Notes of 2021 Winter (Old, link)

Lecture Notes of 2022 Winter (Ongoing)

	Section 1	Theories of Geometry
1/4	Curve Theory, PDF (2022 ver), Annotated PDF (2021 ver)	overview of the course, logistics, curve theory
1/7 1/11	Curve Theory (cont), Surface Theory	differential map, normal curvature, principal curvature
1/13	Surface Theory (II)	shape operator, first fundamental form, isometry, fundamental theorem of surfaces
1/18	Mesh and Point Cloud	polygonal mesh, point cloud
1/20	Rotation and SO(3)	rotation matrix, euler angle, angle-axis, quaternion
	Section 2	3D Deep Learning
	Section 2.1	3D Reconstruction
1/25, 1/27	Learning-based MVS	learning-based MVS, NeRF
2/1	Single Image to 3D	EMD, Chamfer, mesh deformation
	Section 2.2	3D Data Understanding
2/3	3D Backbone Networks	Volumetric CNN
2/8	3D Backbone Networks	PointNet
2/10, 2/15	6D Pose Estimation	ICP, Umeyama's method, direct method
2/17	3D Detection
2/22	3D Instance Segmentation	top-down approach, bottom-up approach
2/24	Intrinsics-based Analysis	geodesic distance, dijkstra's algorithm for geodesics, learning-based method for geodesics, applications
	Section 2.3	Structured 3D Learning
3/3	Deformation Models	surface deformation, space deformation, skeleton skinning
	Section 2.4	Geometry Processing and Collection Analysis
3/8	Surface Reconstruction	explicit and implicit method for surface reconstruction