Recent temporal LiDAR-based 3D object detectors achieve promising performance
based on the two-stage proposal-based approach. They generate 3D box candidates
from the first-stage dense detector, followed by different temporal aggregation
methods. However, these approaches require per-frame objects or whole point
clouds, posing challenges related to memory bank utilization. Moreover, point
clouds and trajectory features are combined solely based on concatenation,
which may neglect effective interactions between them. In this paper, we
propose a point-trajectory transformer with long short-term memory for
efficient temporal 3D object detection. To this end, we only utilize point
clouds of current-frame objects and their historical trajectories as input to
minimize the memory bank storage requirement. Furthermore, we introduce modules
to encode trajectory features, focusing on long short-term and future-aware
perspectives, and then effectively aggregate them with point cloud features. We
conduct extensive experiments on the large-scale Waymo dataset to demonstrate
that our approach performs well against state-of-the-art methods. Code and
models will be made publicly available at this https URL

提出了一种基于时间 LiDAR 的三维物体检测方法，利用点云和轨迹特征相结合的点轨迹变压器和长短期记忆，实现了对三维物体的高效检测。对大规模 Waymo 数据集进行了广泛实验，证明了该方法在性能上优于现有的方法。

PTT：用于高效时序三维物体检测的点轨迹变换器

PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object  Detection

Existing offboard 3D detectors always follow a modular pipeline design to
take advantage of unlimited sequential point clouds. We have found that the
full potential of offboard 3D detectors is not explored mainly due to two
reasons: (1) the onboard multi-object tracker cannot generate sufficient
complete object trajectories, and (2) the motion state of objects poses an
inevitable challenge for the object-centric refining stage in leveraging the
long-term temporal context representation. To tackle these problems, we propose
a novel paradigm of offboard 3D object detection, named DetZero. Concretely, an
offline tracker coupled with a multi-frame detector is proposed to focus on the
completeness of generated object tracks. An attention-mechanism refining module
is proposed to strengthen contextual information interaction across long-term
sequential point clouds for object refining with decomposed regression methods.
Extensive experiments on Waymo Open Dataset show our DetZero outperforms all
state-of-the-art onboard and offboard 3D detection methods. Notably, DetZero
ranks 1st place on Waymo 3D object detection leaderboard with 85.15 mAPH (L2)
detection performance. Further experiments validate the application of taking
the place of human labels with such high-quality results. Our empirical study
leads to rethinking conventions and interesting findings that can guide future
research on offboard 3D object detection.

通过引入 DetZero 模式，提出了一种离线追踪器和多帧检测器定位的离板 3D 目标检测新范式，引入注意机制细化模块以跨长期时间轴加强上下文信息交互，并在 Waymo 数据集上实验证明其显着优于当下所有状态 - of-the-art onboard 和 offboard 的 3D 检测方法，并取得了 85.15 mAPH (L2) 的检测效果。