Is it possible to generate depth map from graycode sequence without using printed calibration patterns?
... if the camera's intrinsics are known and calibrated, is it possible to generate the depthmap fully automatically through a graycode sequence without any manual calibration process ?


Yes, provided that you are talking about a stereo-vision system.

Simply extracting the depth information, does not require knowledge of the camera model. It only requires knowledge of the pixel positions that refer to the same point in space and this is what the Gray code pattern does.

Every Gray code pattern that is projected on to the image "tags" a certain point in space. It is also useful to think about Gray code patterns as bit planes. During the "encoding" process, you segment the image pixels and assign them to bit planes. In this way, every point in space is addressed by a unique binary number that is formed by stacking the bit planes.

If you repeat this process for the left and right images, you will obtain two pairs of $x,y$ coordinates that will tell you where a particular point in space appears to be from the viewpoint of each camera. This is enough to work out "depth" by triangulation.

Knowing the intrinsic parameters allows you to relate this "pixel" depth map to physical coordinates.

For more information, please see this (or any other relevant introduction to imaging using structured light) and this.

Hope this helps.

EDIT:

The basic formula for recovering depth is $Z = f \frac{D}{d_p}$ where:


$f$ is the focal distance of the camera lenses.
$D$ is the distance between the cameras providing the stereo vision
$d_p$ is the "disparity" or relative difference of the position of two pixels associated with the same region in the two images.
$Z$ is the distance from the baseline plane which in turn depends on the relative placement of the two cameras.


Now, if the cameras are not fully calibrated, it is impossible to relate $Z$ with reality as the relative distances would not be preserved. BUT, in an extreme case, you can set $D=1$ and obtain a depth map that gives you some information about the depth of the scene. This should "work" for reasonable assumptions of rectilinear lens and aligned cameras and probably return enough information to discriminate between a ball and a cube, if that is the interest of the application here (?)