  Real-time detection of objects in the 3D scene is one of the tasks an
autonomous agent needs to perform for understanding its surroundings. While
recent Deep Learning-based solutions achieve satisfactory performance, their
high computational cost renders their application in real-life settings in
which computations need to be performed on embedded platforms intractable. In
this paper, we analyze the efficiency of two popular voxel-based 3D object
detection methods providing a good compromise between high performance and
speed based on two aspects, their ability to detect objects located at large
distances from the agent and their ability to operate in real time on embedded
platforms equipped with high-performance GPUs. Our experiments show that these
methods mostly fail to detect distant small objects due to the sparsity of the
input point clouds at large distances. Moreover, models trained on near objects
achieve similar or better performance compared to those trained on all objects
in the scene. This means that the models learn object appearance
representations mostly from near objects. Our findings suggest that a
considerable part of the computations of existing methods is focused on
locations of the scene that do not contribute with successful detection. This
means that the methods can achieve a speed-up of $40$-$60\%$ by restricting
operation to near objects while not sacrificing much in performance.
