  Robust 3D object detection remains a pivotal concern in the domain of
autonomous field robotics. Despite notable enhancements in detection accuracy
across standard datasets, real-world urban environments, characterized by their
unstructured and dynamic nature, frequently precipitate an elevated incidence
of false positives, thereby undermining the reliability of existing detection
paradigms. In this context, our study introduces an advanced post-processing
algorithm that modulates detection thresholds dynamically relative to the
distance from the ego object. Traditional perception systems typically utilize
a uniform threshold, which often leads to decreased efficacy in detecting
distant objects. In contrast, our proposed methodology employs a Neural Network
with a self-adaptive thresholding mechanism that significantly attenuates false
negatives while concurrently diminishing false positives, particularly in
complex urban settings. Empirical results substantiate that our algorithm not
only augments the performance of 3D object detection models in diverse urban
and adverse weather scenarios but also establishes a new benchmark for adaptive
thresholding techniques in field robotics.
