The KITTI Vision Benchmark Suite

In the field of autonomous driving, monocular 3D
detection is a critical task which estimates 3D
properties (depth, dimension, and orientation) of
objects in a single RGB image. Previous works have
used features in a heuristic way to learn 3D
properties, without considering that inappropriate
features could have adverse effects. In this paper,
sample selection is introduced that only suitable
samples should be trained to regress the 3D
properties. To select samples adaptively, we propose
a Learnable Sample Selection (LSS) module, which is
based on Gumbel-Softmax and a relative-distance
sample divider. The LSS module works under a warm-up
strategy leading to an improvement in training
stability. Additionally, since the LSS module
dedicated to 3D property sample selection relies on
object-level features, we further develop a data
augmentation method named MixUp3D to enrich 3D
property samples which conforms to imaging
principles without introducing ambiguity.

Nono

@inproceedings{monolss,
title={MonoLSS: Learnable Sample Selection For
Monocular 3D Detection},
author={Li, Zhenjia and Jia, Jinrang and Shi,
Yifeng},
booktitle={International Conference on 3D Vision},
year={2024}
}