Developing high-performance, real-time architectures for LiDAR-based 3D
object detectors is essential for the successful commercialization of
autonomous vehicles. Pillar-based methods stand out as a practical choice for
onboard deployment due to their computational efficiency. However, despite
their efficiency, these methods can sometimes underperform compared to
alternative point encoding techniques such as Voxel-encoding or PointNet++. We
argue that current pillar-based methods have not sufficiently captured the
fine-grained distributions of LiDAR points within each pillar structure.
Consequently, there exists considerable room for improvement in pillar feature
encoding. In this paper, we introduce a novel pillar encoding architecture
referred to as Fine-Grained Pillar Feature Encoding (FG-PFE). FG-PFE utilizes
Spatio-Temporal Virtual (STV) grids to capture the distribution of point clouds
within each pillar across vertical, temporal, and horizontal dimensions.
Through STV grids, points within each pillar are individually encoded using
Vertical PFE (V-PFE), Temporal PFE (T-PFE), and Horizontal PFE (H-PFE). These
encoded features are then aggregated through an Attentive Pillar Aggregation
method. Our experiments conducted on the nuScenes dataset demonstrate that
FG-PFE achieves significant performance improvements over baseline models such
as PointPillar, CenterPoint-Pillar, and PillarNet, with only a minor increase
in computational overhead.

为了成功商业化自动驾驶汽车，开发基于 LiDAR 的高性能实时架构对于至关重要。本文介绍了一种名为 FG-PFE 的新型 pillar 编码架构，它利用 STV 网格来捕捉每个 pillar 内 LiDAR 点云的细粒度分布。通过 STV 网格，每个 pillar 内的点云分别使用垂直、时间和水平维度进行编码，并通过 Attentive Pillar Aggregation 方法进行聚合。实验结果表明，FG-PFE 相较于基线模型如 PointPillar，CenterPoint-Pillar 和 PillarNet，在计算开销仅略微增加的情况下取得了显著的性能改进。

通过时空虚拟网格进行细粒度柱特征编码的 3D 物体检测

Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid  for 3D Object Detection

Widely-used LiDAR-based 3D object detectors often neglect fundamental
geometric information readily available from the object proposals in their
confidence estimation. This is mostly due to architectural design choices,
which were often adopted from the 2D image domain, where geometric context is
rarely available. In 3D, however, considering the object properties and its
surroundings in a holistic way is important to distinguish between true and
false positive detections, e.g. occluded pedestrians in a group. To address
this, we present GACE, an intuitive and highly efficient method to improve the
confidence estimation of a given black-box 3D object detector. We aggregate
geometric cues of detections and their spatial relationships, which enables us
to properly assess their plausibility and consequently, improve the confidence
estimation. This leads to consistent performance gains over a variety of
state-of-the-art detectors. Across all evaluated detectors, GACE proves to be
especially beneficial for the vulnerable road user classes, i.e. pedestrians
and cyclists.

在广泛使用的基于 LiDAR 的三维物体检测中，由于架构设计选择通常是从二维图像领域采用，很容易忽略从物体提案中获得的基本几何信息。而在三维中，综合考虑物体属性和其周围环境对于区分真伪正检测是重要的，为此我们提出了 GACE，这是一种直观高效的方法来改善给定黑盒三维物体检测器的置信度估计，在各种先进检测器中表现出相对一致的性能提升。在所有评估的检测器中，GACE 在易受伤害的道路使用者类别（即行人和骑车人）中表现尤为有益。

GACE：基于几何感知的 LiDAR 数据黑盒 3D 物体检测器的置信度增强

GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object  Detectors on LiDAR-Data

Current LiDAR-based 3D object detectors for autonomous driving are almost
entirely trained on human-annotated data collected in specific geographical
domains with specific sensor setups, making it difficult to adapt to a
different domain. MODEST is the first work to train 3D object detectors without
any labels. Our work, HyperMODEST, proposes a universal method implemented on
top of MODEST that can largely accelerate the self-training process and does
not require tuning on a specific dataset. We filter intermediate pseudo-labels
used for data augmentation with low confidence scores. On the nuScenes dataset,
we observe a significant improvement of 1.6% in AP BEV in 0-80m range at
IoU=0.25 and an improvement of 1.7% in AP BEV in 0-80m range at IoU=0.5 while
only using one-fifth of the training time in the original approach by MODEST.
On the Lyft dataset, we also observe an improvement over the baseline during
the first round of iterative self-training. We explore the trade-off between
high precision and high recall in the early stage of the self-training process
by comparing our proposed method with two other score filtering methods:
confidence score filtering for pseudo-labels with and without static label
retention. The code and models of this work are available at
this https URL

该研究使用 MODEST 训练 3D 物体探测器，提出了 HyperMODEST 方法，通过筛选中间的伪标签来进行数据增强，加速了自我训练的过程，同时在不需要在特定的数据集上进行调整，取得了显著的性能提升。