LiDAR-based sparse 3D object detection plays a crucial role in autonomous
driving applications due to its computational efficiency advantages. Existing
methods either use the features of a single central voxel as an object proxy,
or treat an aggregated cluster of foreground points as an object proxy.
However, the former lacks the ability to aggregate contextual information,
resulting in insufficient information expression in object proxies. The latter
relies on multi-stage pipelines and auxiliary tasks, which reduce the inference
speed. To maintain the efficiency of the sparse framework while fully
aggregating contextual information, in this work, we propose SparseDet which
designs sparse queries as object proxies. It introduces two key modules, the
Local Multi-scale Feature Aggregation (LMFA) module and the Global Feature
Aggregation (GFA) module, aiming to fully capture the contextual information,
thereby enhancing the ability of the proxies to represent objects. Where LMFA
sub-module achieves feature fusion across different scales for sparse key
voxels %which does this through via coordinate transformations and using
nearest neighbor relationships to capture object-level details and local
contextual information, GFA sub-module uses self-attention mechanisms to
selectively aggregate the features of the key voxels across the entire scene
for capturing scene-level contextual information. Experiments on nuScenes and
KITTI demonstrate the effectiveness of our method. Specifically, on nuScene,
SparseDet surpasses the previous best sparse detector VoxelNeXt by 2.2\% mAP
with 13.5 FPS, and on KITTI, it surpasses VoxelNeXt by 1.12\%
$\mathbf{AP_{3D}}$ on hard level tasks with 17.9 FPS.

提出了一种基于稀疏查询的 SparseDet 稀疏三维物体检测方法，通过局部多尺度特征聚合模块和全局特征聚合模块，完整地捕捉了上下文信息，提高了代理物体表达能力。实验结果在 nuScenes 和 KITTI 上验证了该方法的有效性。

SparseDet: 一种简单而有效的基于全稀疏 LiDAR 的 3D 物体检测框架

SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based  3D Object Detection

In this paper, we propose SparseDet for end-to-end 3D object detection from
point cloud. Existing works on 3D object detection rely on dense object
candidates over all locations in a 3D or 2D grid following the mainstream
methods for object detection in 2D images. However, this dense paradigm
requires expertise in data to fulfill the gap between label and detection. As a
new detection paradigm, SparseDet maintains a fixed set of learnable proposals
to represent latent candidates and directly perform classification and
localization for 3D objects through stacked transformers. It demonstrates that
effective 3D object detection can be achieved with none of post-processing such
as redundant removal and non-maximum suppression. With a properly designed
network, SparseDet achieves highly competitive detection accuracy while running
with a more efficient speed of 34.5 FPS. We believe this end-to-end paradigm of
SparseDet will inspire new thinking on the sparsity of 3D object detection.

本文提出了 SparseDet 这一端到端的基于点云的三维物体检测算法，通过固定数量的可学习建议来代表潜在候选框，并使用堆叠的 transformers 直接进行三维物体的分类和定位，且无需后处理步骤，相比传统方法具有更高的效率和准确率。