  The accurate modeling of indoor radio propagation is crucial for
localization, monitoring, and device coordination, yet remains a formidable
challenge, due to the complex nature of indoor environments where radio can
propagate along hundreds of paths. These paths are resulted from the room
layout, furniture, appliances and even small objects like a glass cup. They are
also influenced by the object material and surface roughness. Advanced machine
learning (ML) techniques have the potential to take such non-linear and
hard-to-model factors into consideration. However, extensive and fine-grained
datasets are urgently required. This paper presents WiSegRT, an open-source
dataset for indoor radio propagation modeling. Generated by a differentiable
ray tracer within the segmented 3-dimensional (3D) indoor environments, WiSegRT
provides site-specific channel impulse responses for each grid point relative
to the given transmitter location. We expect WiSegRT to support a wide-range of
applications, such as ML-based channel prediction, accurate indoor
localization, radio-based object detection, wireless digital twin, and more.
