发表机构
CSIRO; The University of Hong Kong（澳大利亚联邦科学与工业研究组织; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出免训练可微优化框架，将时间平滑约束融入配准目标，用L-BFGS联合求解连续边界框，在良好可见性下显著降低粗糙度，并揭示可见性限制及速度噪声导致的性能边界。
AI 中文摘要
针对LiDAR点云流细化一系列粗三维边界框，需要几何精确（高IoU）且时间连贯（低粗糙度）的轨迹，最好无需训练数据。通常的做法将这两个关注点分开处理：独立配准每一帧，然后用Kalman-RTS或Savitzky-Golay滤波器对轨迹进行平滑。平滑会使边界框偏离几何最优位置，且不再重新优化，因此以精度换取平滑度。我们转而将时间平滑约束纳入免训练的配准目标中，并使用L-BFGS联合求解所有姿态。收益取决于目标被观察到的程度。在观察良好的轨迹上收益显著：在低粗糙度预算内，联合目标在多随机种子的配对统计上优于两种事后平滑器，并在匹配精度下将粗糙度相对于逐帧配准降低数倍。将可见性作为实验变量揭示了其极限。随着视角变为单侧，优势单调衰减，直至对近边缘物体与零无显著差异，并在具有距离相关密度和自运动的射线投射模拟器下略为负值，此时解耦流程在严格粗糙度预算下实际上领先。我们定位了这一边界，并将其追溯到一个项：方向对齐将偏航角与估计速度绑定，一旦速度估计有噪声，该项即失效。一个无需真值的规则可以选择时间尺度，并使每条轨迹保持在粗糙度预算内。
英文摘要
Refining a sequence of coarse 3D bounding boxes against a LiDAR point-cloud stream demands tracks that are geometrically accurate (high IoU) and temporally coherent (low roughness), preferably without training data. The usual recipe keeps the two concerns apart: register each frame independently, then smooth the trajectory afterwards with a Kalman~RTS or Savitzky--Golay filter. Smoothing displaces boxes from a geometric optimum and never re-optimises, so it trades accuracy for smoothness. We instead fold the temporal smoothness constraint into a training-free registration objective and solve for all poses jointly with L-BFGS. The payoff depends on how well the object is seen. On well-observed tracks it is large: within the low-roughness budget, the joint objective beats both post-hoc smoothers on paired multi-seed statistics and cuts roughness several-fold relative to frame-wise registration at matched accuracy. Treating visibility as an experimental variable exposes the limit. The advantage decays monotonically as views become one-sided, until it is indistinguishable from zero for near-edge-on objects and slightly negative under a ray-cast simulator with range-dependent density and ego motion, where the decoupled pipeline is in fact ahead at tight roughness budgets. We locate that boundary and trace it to one term: orientation alignment ties yaw to the estimated velocity and fails once that estimate is noisy. A ground-truth-free rule can choose the temporal scale and keep every track inside the roughness budget.
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