  Point clouds have been a recent interest for ray tracing-based radio channel
characterization, as sensors such as RGB-D cameras and laser scanners can be
utilized to generate an accurate virtual copy of a physical environment. In
this paper, a novel ray launching algorithm is presented, which operates
directly on noisy point clouds acquired from sensor data. It produces coarse
paths that are further refined to exact paths consisting of reflections and
diffractions. A commercial ray tracing tool is utilized as the baseline for
validating the simulated paths. A significant majority of the baseline paths is
found. The robustness to noise is examined by artificially applying noise along
the normal vector of each point. It is observed that the proposed method is
capable of adapting to noise and finds similar paths compared to the baseline
path trajectories with noisy point clouds. This is prevalent especially if the
normal vectors of the points are estimated accurately. Lastly, a simulation is
performed with a reconstructed point cloud and compared against channel
measurements and the baseline paths. The resulting paths demonstrate similarity
with the baseline path trajectories and exhibit an analogous pattern to the
aggregated impulse response extracted from the measurements. Code available at
https://github.com/nvaara/NimbusRT
