Reproduce “A Differentiable Optimization Framework for Registering Sequential Bounding Boxes with Point Cloud Stream” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Xuesong Li, Jinguang Tong, Jie Hong
The paper presents a training-free method that jointly refines sequences of 3D bounding boxes from LiDAR points, optimizing fit and temporal smoothness with L-BFGS rather than smoothing frames afterward. In simulated broad-visibility tracks, it improves low-roughness-budget 3D volumetric intersection-over-union over post-hoc smoothers by 0.032 and 0.059, but the advantage vanishes or reverses under one-sided views, not establishing real-LiDAR performance.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.