Shih-Chin

When I did homework assignments of the famous Deep Learning course CS231n from Stanford, I was so impressed by 100X↑ performance boost by using broadcasting mechanism in NumPy. However, broadcasting doesn’t always speed up computation, we should also take into account memory usage and memory access pattern [2], or we will get a slower execution instead. This post shows several experiments and my reasoning of why certain operation is performant or not.

For many of my artist colleagues who want to learn scripting in Maya, one of their motivations is mostly to automate repetitive or time-consuming manual tasks. Since Maya can show all command history of our user interactions in Script Editor. Therefore, if we know how to perform target instructions manually, we could write a script of all corresponding commands. But finding the equivalent commands is not straightforward in some situations. Besides looking up documentation, we need other strategies.

Radial Basis Functions (RBFs) is one of the commonly used methods to interpolate multi-dimensional data. RBFs creates smooth and less oscillating interpolation than inverse distance weighting (IDW) does. It has many applications in Computer Graphics, such as surface reconstruction [3], animation blending [1], facial retargeting, color calibration [4], and etc. Despite of the variety of its applications, those applications all have a common theme called scattered data interpolation.

I have been using DSLR for over 10 years, I thought I knew few knowledge about photography. But after I watched a wonderful online course of digital photography by Prof. Marc Levoy [1], I just found there were so many details I weren’t aware before. The thing embarrassed me the most was that I incorrectly related f-number to lens aperture size. This post is trying to introduce the idea of f-number starting from scene radiance.

When I decided to be a guest lecturer at NCKU last year, I kept asking myself what is the most important thing I wish I’d learned in school. I carefully reviewed my current skill sets and found most of my skills were developed at work. Besides sharing knowledge about Computer Graphics, I think there is a more fundamental competency we need to develop in school, that is continuous learning.

I was privileged to be a guest lecturer to give five lectures about Computer Graphics at National Cheng Kung University (NCKU) in Taiwan. When I prepared the course materials, I deeply realized again that the best way to learn a topic is to teach someone else (especially to a person who never heard about Computer Graphics before). In order to avoid teaching wrong ideas, I had carefully reviewed what I known from ground up and derived each mathematical equation in the textbooks. This post is about what I’ve learned in those few months.