MotionSync：面向标签高效三维感知的因果跟踪器的非因果优化

中文摘要 AI 辅助

三维框标注与轨迹标注是自动驾驶数据引擎中的成本瓶颈，用于缓解该问题的离线系统会完全替代在线感知栈，因此同时需要两种模式的团队需维护并协调两套系统。MotionSync将因果/非因果边界明确为架构接缝。基于强大已发布基线构建的严格因果跟踪器，经创新驱动的不确定性校准、帧率不变的运动关联门及带学习模式选择的多假设运动扩展后，可输出有效的在线结果。非因果通路随后对缓冲轨迹进行优化：分别对位姿、范围和偏航角应用Rauch–Tung–Striebel平滑、经物理验证的间隙补全，以及针对LiDAR点标签的幽灵轨迹语义修剪。优化器不会回写，因此一套系统可服务两种模式，且优化效果是未改动的因果估计的增量。作为自动标注器，在Waymo数据集上，固定三维检测器经25%人工标签加MotionSync伪标签训练后，达到全监督平均精度均值（mAP）的96.9%；在10%预算下，非因果通路使伪标签比同一跟踪器因果阶段的伪标签提升3.3 mAP/L2。将在线跟踪器重新拟合其自身优化后的输出，可恢复73%的人工监督收益，而其因果输出作为监督效果比完全不重新拟合更差。作为跟踪器，MotionSync在核心指标上与领先的已发布离线条目持平，且在误差构成上领先，优化通路可同时减少漏检和碎片化，这是间隙补全的特征，而非调优检测器的特征。

英文摘要

Three-dimensional box-and-track annotation is the cost bottleneck in autonomous-driving data engines, and the offline systems built to relieve it replace the online perception stack outright, so a team needing both regimes maintains and reconciles two. MotionSync makes the causal/non-causal boundary an explicit architectural seam instead. A strictly causal tracker, built on a strong published baseline and extended with innovation-driven uncertainty calibration, frame-rate-invariant kinematic association gates, and multi-hypothesis motion with learned mode selection, emits a valid online result. A non-causal pass then revises the buffered trajectories with Rauch--Tung--Striebel smoothing applied separately to pose, extent and yaw, physics-validated gap completion, and semantic pruning of ghost tracks against LiDAR point labels. The refiner never writes back, so one system serves both regimes and refinement's effect is a delta over an unaltered causal estimate. Used as an auto-labeller, a fixed 3D detector trained on 25% human labels plus MotionSync pseudo-labels reaches 96.9% of its full-supervision mean average precision (mAP) on Waymo, and at a 10% budget the non-causal pass accounts for +3.3 mAP/L2 over pseudo-labels from the same tracker's causal stage. Re-fitting the online tracker on its own refined output recovers 73% of the benefit of human supervision, while its causal output is worse supervision than no re-fitting at all. As a tracker MotionSync is at parity with the leading published offline entries on the headline metric and ahead of them on error composition, which is where a refinement pass can act at all: it reduces misses and fragmentations together, the signature of gap completion rather than of a tuned detector.

发表机构

• Qualcomm Technologies, Inc（高通技术公司）

机构由 AI 辅助整理，请以论文原文为准。