Is a Kalman filter suitable to filter projected points positions, given Euler angles of the capturing device?
Low Pass Filtering

It would be good to know what you mean by "simple low pass filter".

For example, if your measurements at time $k$ are 

$$
p_{k} = \left[ \begin{array}{c} x_k \\ y_k \end{array} \right]
$$

and your low pass filtered estimates are:

$$
p^{\tt LPF}_k = \alpha p^{\tt LPF}_{k-1} + (1-\alpha)p_k
$$

then you will have quite a large group delay in the filter of about $1/(1-\alpha)$ (for alpha close to 1).  

Modeling the Signal: Simplistic Approach

To use the Kalman filter (or any similar approach), you need to have a model for how your measurements are acquired and updated.

Usually this looks like:

$$
p^{\tt TRUE}_{k+1} = {\mathbf A} p^{\tt TRUE}_k + {\mathbf B} \epsilon_k
$$
where $\epsilon_k$ is the process (driving) noise, ${\mathbf A}$ is the state transition matrix, and ${\mathbf B}$ is your input matrix.

And then your measured $p_k$ are:
$$
p_k = {\mathbf C} p^{\tt TRUE}_k + {\mathbf D} \nu_k
$$
where $\nu_k$ is the output (measurement) noise,  ${\mathbf C}$ is the output matrix, and ${\mathbf D}$ is your measurement noise matrix.

Here, the "state" of the model is chosen as the true positions, and the things you measure are the output.

You can then apply the Kalman filter equations to this to get state estimates $\hat{p^{\tt TRUE}_k }$ of the true position.

However, this approach is simplistic because it does not use any knowledge of how the points might move (nor does it use your 4 points and any knowledge you might have about how they move together).

Modeling the Signal: Starting a Better Approach

This page shows how to set up the problem involving the positions and euler angles.
It's doing something different from what you need, but the state is:

$$
p^{\tt TRUE}_{k} = \left[ 
x_k\  y_k\  z_k\  
\dot{x}_k\  \dot{y}_k\  \dot{z}_k\  
\ddot{x}_k\  \ddot{y}_k\  \ddot{z}_k\  
\phi\ \psi\ \theta\ 
\dot{\phi}\ \dot{\psi}\ \dot{\theta}\ 
\ddot{\phi}\ \ddot{\psi}\ \ddot{\theta}\ 
 \right]^T
$$

and the measurements (output) is 

$$
p_k = \left[ x_k\  y_k\  z_k\  \phi\ \psi\ \theta\ \right ]^T
$$

All the model on that page is really doing is saying:
$$
x^{\tt TRUE}_k = \sum_{n=0}^k \dot{x}^{\tt TRUE}_n n\Delta t + \frac{1}{2}  \sum_{n=0}^k \ddot{x}^{\tt TRUE}_n (n\Delta t)^2
$$
(but for each of $x,y,$ and $z$).

This is just the classic "equations of motion". See equation (3) here.