  This study addresses the limitations of single-viewpoint observations of
Coronal Mass Ejections (CMEs) by presenting results from a 3D catalog of 360
CMEs during solar cycle 24, fitted using the GCS model. The dataset combines
326 previously analyzed CMEs and 34 newly examined events, categorized by their
source regions into active region (AR) eruptions, active prominence (AP)
eruptions, and prominence eruptions (PE). Estimates of errors are made using a
bootstrapping approach. The findings highlight that the average 3D speed of
CMEs is $\sim$1.3 times greater than the 2D speed. PE CMEs tend to be slow,
with an average speed of 432 km $s^{-1}$. AR and AP speeds are higher, at 723
km $s^{-1}$ and 813 km $s^{-1}$, respectively, with the latter having fewer
slow CMEs. The distinctive behavior of AP CMEs is attributed to factors like
overlying magnetic field distribution or geometric complexities leading to less
accurate GCS fits. A linear fit of projected speed to width gives a gradient of
2 km $s^{-1}deg^{-1}$, which increases to 5 km $s^{-1}deg^{-1}$ when the
GCS-fitted `true' parameters are used. Notably, AR CMEs exhibit a high gradient
of 7 km $s^{-1}deg^{-1}$, while AP CMEs show a gradient of 4 km
$s^{-1}deg^{-1}$. PE CMEs, however, lack a significant speed-width
relationship. We show that fitting multi-viewpoint CME images to a geometrical
model such as GCS is important to study the statistical properties of CMEs, and
can lead to a deeper insight into CME behavior that is essential for improving
future space weather forecasting.
