Taught by Philip Maddock, this course will cover a variety of common techniques and practices to get you productive in 3DEqualizer. We will also visit on some of the less obvious areas of the software, such as working with the python interface and understanding the role of lens grids. Designed as a guide to give people a running start in the application, a working knowledge of other tracking applications is recommended.
Maddock is a 3D artist who has been utilising 3DEqualizer4 for the last four years. In that time he has worked in mainly films and TV commercials and has trained artists in both London and Mumbai. Philip is currently working as an artist at The Mill London and is joining to provide the first ever fxphd course on 3DEqualizer.
Class 1: Intro to the user interface
An introduction to the 3DE interface and a quick look at how we can use python to boost productivity.
Class 2: 2D tracking
We will explore the multifaceted nature of the 3DE 2D points.
Class 3: Tracking nodal shots
We will take what we have learned already and apply it tracking several nodal shots, each with their own challenges.
Class 4: Tracking free moves
We will progress on from our previous lessons by tracking and solving a free moving shot. We will end the class by aligning the scene ready for export.
Class 5: Object tracking
Here we will learn how to track and control objects. This will be out first major introduction to manipulating 3DE's 3D environment.
Class 6: Using geometry
Using geometry to influence a camera track is a vital skill. We will look at using prebuilt geo then move onto explore the correct use 3DE’s own primitives.
Class 7: Building a small scene from reference images
We will learn how to build point clouds of objects and environments by using reference frames, then learn how to use this data to influence our moving camera tracks.
Class 8: Lens distortion
Lens distortion is one of the most commonly misunderstood practices in tracking. We will learn how to tackle it effectively by building up a fundamental understanding.
Class 9: Survey data
Using real-world measurements to influence a camera track.
Class 10: Lidar
Using a real world scan to influence a camera track.