Making a car in 3DS Max

I started off making the model of Audi R8 when I was in 10th class. Since it was a beginning in a big project, it wasn’t very good but better than what I had expected. I used box modelling, i.e, started off with a simple box with multiple edges and carved out the shape accordingly. The result is:

The rendering was done using Mental ray render engine. Simple material was used without any environment, except for those walls. I also used few omnis to illuminate it from all directions.

After one year, I started off with Lamborgini Gallardo. Since my enthusiasm keeps dropping and I have a hectic schedule at the college, it took me one long year to complete it.

The gallardo was designed using a completely different technique. It was designed using what I call panel modelling. The model was broken into panels, each was designed independently and finally everything was sewn together again. This gives more control over the flow of the model, and makes it less cumbersome. There are no cons of this method as far as I can tell. Here is the result:


This time, only skylight was used. HDRI map was used for environment. Mental Ray car paint was used for the body. Clearly, the car looks less cumbersome and more real compared to Audi R8.

The guide images for the car were obtained from Blueprints


Image registration using python

As per wikipedia, ‘Image registration is the process of transforming different sets of data into one coordinate system’. Simply put, image registration is comparing images with a base image and quantifying the changes. For example when a video is streamed, we would like to find out whether the camera is changing its position, and if so, how do you correct it.

We will look at a snippet written in python which compares two images and returns a homography matrix which relates both the images.

A homography matrix is a 3×3 matrix which decides the transformation between two set of coordinate systems.Mathematically, if A is a set of coordinates in one space and B is a set of corresponding coordinates in another space, then

A = HB

Wher H is our homography matrix.

To calculate our homography matrix,we need a set of corresponding points in the two spaces. For this, SIFT(Scale Invariant Feature Transform) is used. SIFT generates a set of ‘interest’ points which can be used for tracking an image.

Once we get the corresponding points, we can calculate the homography matrix. But we need a good algorithm which will pick the necessary points and discard the outliers. To implement this, we use the RANSAC algorithm. RANSAC(RANdom SAmple Consensus) algorithm picks a minimum number of points from a given set and estimates the model. It then takes into account more points from the set which closely fit the model and then calculates the error in the estimated model. This is done for a predefined number of times to get a good model. This model ensures that only inliers are included and outliers are not taken into account.

Using SIFT implementation in python and calculation of homography matrix in python, we apply a RANSAC algorithm to find the homography matrix and change the first image accordingly so that it matches the orientation of the second image.

You can find the source code here:

Python image registration

For further study:
1. Image registration
2. Scale Invariant Feature Transform

Note: SIFT is a patented technique by David Lowe. It is protected by the following patents:
Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image
David G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application filed March 8, 1999. Asignee: The University of British Columbia