  Learning-based methods to solve dense 3D vision problems typically train on
3D sensor data. The respectively used principle of measuring distances provides
advantages and drawbacks. These are typically not compared nor discussed in the
literature due to a lack of multi-modal datasets. Texture-less regions are
problematic for structure from motion and stereo, reflective material poses
issues for active sensing, and distances for translucent objects are intricate
to measure with existing hardware. Training on inaccurate or corrupt data
induces model bias and hampers generalisation capabilities. These effects
remain unnoticed if the sensor measurement is considered as ground truth during
the evaluation. This paper investigates the effect of sensor errors for the
dense 3D vision tasks of depth estimation and reconstruction. We rigorously
show the significant impact of sensor characteristics on the learned
predictions and notice generalisation issues arising from various technologies
in everyday household environments. For evaluation, we introduce a carefully
designed dataset\footnote{dataset available at
https://github.com/Junggy/HAMMER-dataset} comprising measurements from
commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular
RGB+P. Our study quantifies the considerable sensor noise impact and paves the
way to improved dense vision estimates and targeted data fusion.
