NovaSplat：基于3D高斯溅射（3DGS）的无模型6D物体位姿估计的引入与优化
NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement
AI总结：
NovaSplat是一种无模型6D物体位姿估计流水线，通过3D高斯溅射模型实现，在家庭和工业基准上性能优异，还推出了速度提升3.1倍的轻量版本。
摘要：
无CAD模型的新物体6D位姿估计仍是一个开放挑战：无模型方法必须在引入阶段从零构建物体表示，且长期落后于基于模型的方法。我们提出NovaSplat，一种缩小该差距的无模型流水线。从引入阶段拍摄的一段智能手机短视频中，我们训练一个3D高斯溅射（3DGS）模型，该模型既作为检测和粗位姿估计的模板源，又作为位姿优化的可微骨干。我们的优化器在无梯度奇异性的连续旋转参数化下，结合光度和几何损失优化物体到相机的位姿。在家庭物体基准上，NovaSplat超越所有基于模型的方法；在具有无纹理和镜面表面的挑战性工业基准上，它优于所有无模型竞争对手，同时大幅缩小与基于模型方法的差距。我们还引入NovaSplat-Lite，一种推理变体，速度提升3.1倍且精度损失可忽略。代码将在该https URL提供。
英文摘要：
6D pose estimation of novel objects without CAD models remains an open challenge: model-free methods must build the object representation from scratch during onboarding and have long trailed their model-based counterparts. We present NovaSplat, a model-free pipeline that closes this gap. From a short smartphone video captured at onboarding, we train a 3D Gaussian Splatting (3DGS) model that serves both as a template source for detection and coarse pose estimation and as a differentiable backbone for pose refinement. Our refiner optimizes the object-to-camera pose with combined photometric and geometric losses under a continuous rotation parametrization free of gradient singularities. On a household-object benchmark, NovaSplat surpasses all model-based methods. On a challenging industrial benchmark with textureless and specular surfaces, it outperforms all model-free competitors while substantially narrowing the gap to model-based ones. We further introduce NovaSplat-Lite, an inference variant that is $3.1\times$ faster at negligible accuracy cost. Code will be made available at \href{https://github.com/POSE-Lab/novasplat}{https://github.com/POSE-Lab/novasplat}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
