Learning 3D scene flow from LiDAR point clouds presents significant
difficulties, including poor generalization from synthetic datasets to real
scenes, scarcity of real-world 3D labels, and poor performance on real sparse
LiDAR point clouds. We present a novel approach from the perspective of
auto-labelling, aiming to generate a large number of 3D scene flow pseudo
labels for real-world LiDAR point clouds. Specifically, we employ the
assumption of rigid body motion to simulate potential object-level rigid
movements in autonomous driving scenarios. By updating different motion
attributes for multiple anchor boxes, the rigid motion decomposition is
obtained for the whole scene. Furthermore, we developed a novel 3D scene flow
data augmentation method for global and local motion. By perfectly synthesizing
target point clouds based on augmented motion parameters, we easily obtain lots
of 3D scene flow labels in point clouds highly consistent with real scenarios.
On multiple real-world datasets including LiDAR KITTI, nuScenes, and Argoverse,
our method outperforms all previous supervised and unsupervised methods without
requiring manual labelling. Impressively, our method achieves a tenfold
reduction in EPE3D metric on the LiDAR KITTI dataset, reducing it from $0.190m$
to a mere $0.008m$ error.

通过自动标记生成大量真实 LiDAR 点云的三维场景流伪标签，采用刚体运动假设模拟自治驾驶场景中的潜在物体级刚体运动，通过更新多个锚点框的不同运动属性，获得整个场景的刚体运动分解。在多个真实数据集上测试，方法在无监督和有监督方法中表现出色，无需手动标记。在 LiDAR KITTI 数据集上，我们的方法将 EPE3D 度量值减少了 10 倍，从 0.190 m 降至 0.008 m。

3DSFLabelling: 通过伪自动标注提升三维场景流估计

3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo  Auto-labelling

A major bottleneck to scaling-up training of self-driving perception systems
are the human annotations required for supervision. A promising alternative is
to leverage "auto-labelling" offboard perception models that are trained to
automatically generate annotations from raw LiDAR point clouds at a fraction of
the cost. Auto-labels are most commonly generated via a two-stage approach --
first objects are detected and tracked over time, and then each object
trajectory is passed to a learned refinement model to improve accuracy. Since
existing refinement models are overly complex and lack advanced temporal
reasoning capabilities, in this work we propose LabelFormer, a simple,
efficient, and effective trajectory-level refinement approach. Our approach
first encodes each frame's observations separately, then exploits
self-attention to reason about the trajectory with full temporal context, and
finally decodes the refined object size and per-frame poses. Evaluation on both
urban and highway datasets demonstrates that LabelFormer outperforms existing
works by a large margin. Finally, we show that training on a dataset augmented
with auto-labels generated by our method leads to improved downstream detection
performance compared to existing methods. Please visit the project website for
details this https URL

自动驾驶感知系统训练中的一个主要瓶颈是需要人工标注的监督，而一种有前景的替代方法是利用 “自动标注” 离线感知模型，它能够以较低的成本从原始 LiDAR 点云生成注释。本研究提出了一种简单、高效和有效的轨迹级精化方法 LabelFormer，该方法通过首先单独编码每帧的观测，然后利用自注意力对轨迹进行完整的时间上下文推理，并最终解码出精化的目标尺寸和每帧的位置。在城市和高速公路数据集上的评估表明，LabelFormer 在性能上显著优于现有方法。最后，我们展示了使用我们方法生成的自动标注增强数据集训练的效果，与现有方法相比，下游检测性能得到了改善。

LabelFormer: 基于激光雷达点云的离车感知目标轨迹细化

LabelFormer: Object Trajectory Refinement for Offboard Perception from  LiDAR Point Clouds

Deep learning models for semantic segmentation rely on expensive,
large-scale, manually annotated datasets. Labelling is a tedious process that
can take hours per image. Automatically annotating video sequences by
propagating sparsely labeled frames through time is a more scalable
alternative. In this work, we propose a novel label propagation method, termed
Warp-Refine Propagation, that combines semantic cues with geometric cues to
efficiently auto-label videos. Our method learns to refine geometrically-warped
labels and infuse them with learned semantic priors in a semi-supervised
setting by leveraging cycle consistency across time. We quantitatively show
that our method improves label-propagation by a noteworthy margin of 13.1 mIoU
on the ApolloScape dataset. Furthermore, by training with the auto-labelled
frames, we achieve competitive results on three semantic-segmentation
benchmarks, improving the state-of-the-art by a large margin of 1.8 and 3.61
mIoU on NYU-V2 and KITTI, while matching the current best results on
Cityscapes.

本文提出了一种新的标签传播方法，即 Warp-Refine Propagation，通过将几何和语义线索结合起来，半监督地利用时间轴上的循环一致性，学习精细地修正几何变形的标签并将它们与学习到的语义先验相结合，以高效地自动标注视频序列，这种方法显著提高了标签传播的表现，并实现了在三个语义分割基准上的竞争性结果 improvement。