LiDAR-based 3D object detection plays an essential role in autonomous
driving. Existing high-performing 3D object detectors usually build dense
feature maps in the backbone network and prediction head. However, the
computational costs introduced by the dense feature maps grow quadratically as
the perception range increases, making these models hard to scale up to
long-range detection. Some recent works have attempted to construct fully
sparse detectors to solve this issue; nevertheless, the resulting models either
rely on a complex multi-stage pipeline or exhibit inferior performance. In this
work, we propose SAFDNet, a straightforward yet highly effective architecture,
tailored for fully sparse 3D object detection. In SAFDNet, an adaptive feature
diffusion strategy is designed to address the center feature missing problem.
We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2
datasets. SAFDNet performed slightly better than the previous SOTA on the first
two datasets but much better on the last dataset, which features long-range
detection, verifying the efficacy of SAFDNet in scenarios where long-range
detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous
best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded
2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x
faster. The code will be available at this https URL

通过设计自适应特征扩散策略，提出了一种用于完全稀疏的三维目标检测的高效架构 SAFDNet，并在长距离检测场景中证明了其有效性。

SAFDNet：一种简单有效的全稀疏 3D 物体检测网络

SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object  Detection

Object detection applied to LiDAR point clouds is a relevant task in
robotics, and particularly in autonomous driving. Single frame methods,
predominant in the field, exploit information from individual sensor scans.
Recent approaches achieve good performance, at relatively low inference time.
Nevertheless, given the inherent high sparsity of LiDAR data, these methods
struggle in long-range detection (e.g. 200m) which we deem to be critical in
achieving safe automation. Aggregating multiple scans not only leads to a
denser point cloud representation, but it also brings time-awareness to the
system, and provides information about how the environment is changing.
Solutions of this kind, however, are often highly problem-specific, demand
careful data processing, and tend not to fulfil runtime requirements. In this
context we propose TimePillars, a temporally-recurrent object detection
pipeline which leverages the pillar representation of LiDAR data across time,
respecting hardware integration efficiency constraints, and exploiting the
diversity and long-range information of the novel Zenseact Open Dataset (ZOD).
Through experimentation, we prove the benefits of having recurrency, and show
how basic building blocks are enough to achieve robust and efficient results.

通过时间周期性地利用 LiDAR 数据的柱状表示，TimePillars 可以实现在硬件集成效率约束下利用 Zenseact Open 数据集的多样性和长距离信息，从而实现稳健且高效的目标检测。

TimePillars: 时序循环的 3D LiDAR 物体检测

TimePillars: Temporally-Recurrent 3D LiDAR Object Detection

Recently 3D object detection from surround-view images has made notable
advancements with its low deployment cost. However, most works have primarily
focused on close perception range while leaving long-range detection less
explored. Expanding existing methods directly to cover long distances poses
challenges such as heavy computation costs and unstable convergence. To address
these limitations, this paper proposes a novel sparse query-based framework,
dubbed Far3D. By utilizing high-quality 2D object priors, we generate 3D
adaptive queries that complement the 3D global queries. To efficiently capture
discriminative features across different views and scales for long-range
objects, we introduce a perspective-aware aggregation module. Additionally, we
propose a range-modulated 3D denoising approach to address query error
propagation and mitigate convergence issues in long-range tasks. Significantly,
Far3D demonstrates SoTA performance on the challenging Argoverse 2 dataset,
covering a wide range of 150 meters, surpassing several LiDAR-based approaches.
Meanwhile, Far3D exhibits superior performance compared to previous methods on
the nuScenes dataset. The code will be available soon.

提出了一种名为 Far3D 的新型稀疏查询框架，通过利用高质量的二维对象先验信息生成三维自适应查询，引入了透视感知聚合模块来高效捕获长距离对象在不同视角和尺度上的区别性特征，并通过范围调制的三维去噪方法解决了长距离任务中查询错误传播和收敛问题，相较于基于 LiDAR 的方法，在覆盖范围 150 米的具有挑战性的 Argoverse 2 数据集上表现出最先进的性能，并且在 nuScenes 数据集上相比之前的方法也展示了更好的性能。