Exploiting Label Uncertainty for Enhanced 3D Object Detection from Point Clouds

Accurate detection of objects from LiDAR point
clouds is crucial for autonomous driving and environment
modeling. However, uncertainties in ground truth labels due to
occlusions, sparsity, and truncation can hinder model training
and performance. This paper introduces two strategies to address
these issues: 1) Soft Regression Loss (SoRL) and 2) Discrete
Quantization Sampling (DQS). SoRL utilizes Gaussian distributions
for object predictions, measuring uncertainty based on the
probability of ground truth labels within these distributions. This
method effectively accounts for deviations in object location and
orientation. Meanwhile, DQS introduces uncertainty scores for
dynamic sample selection, aiming to refine the quality of positive
samples for regression. Based on the proposed modules, we design
a lightweight multi-stage object detection framework. Notably,
these modules can enhance existing 3D object detection methods
without affecting significantly inference speeds. Experiments
over benchmark datasets show the effectiveness of our method,
especially for cars in sparse point clouds.