MooMIns -- 多实例单目3D重建与物体姿态估计
MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances
AI总结：
提出MooMIns方法，利用单张图像中同一物体的多个实例提供隐式多视图几何，通过反向高斯泼溅实现3D重建和6D姿态估计，避免深度先验导致的幻觉。
摘要：
从单张单目图像同时进行3D重建和6D物体姿态估计是一个固有的病态问题。然而，在工业场景中，物体的多个实例通常随机堆叠在料箱中，隐式地在单张图像内提供了同一物体的多个视角。我们证明，这种隐式多视图几何可以被利用来同时重建物体的3D形状并估计每个可见物体实例的6D姿态。我们提出了MooMIns，一种基于高斯泼溅的新方法，它反转了原始高斯泼溅公式：不是从多个相机渲染单个场景，而是从单个相机渲染多个物体实例。我们的方法使用SAM3实例分割掩码和改进的运动恢复结构（SfM）流水线进行初始化。与学习的单目深度估计不同，我们基于图像证据进行真正的几何重建，避免了训练数据先验导致的幻觉。我们在合成和真实料箱抓取场景中评估MooMIns，并展示了对于未见过的物体的准确重建以及单个实例的可靠姿态估计。
英文摘要：
Simultaneous 3D reconstruction and 6D object pose estimation from a single monocular image is an inherently ill-posed problem. In industrial settings, however, multiple instances of an object are often randomly arranged in bins, implicitly providing several views of the same object within a single image. We show that this implicit multi-view geometry can be exploited to simultaneously reconstruct the object in 3D and estimate the 6D pose of each visible object instance. We present MooMIns, a new Gaussian-splatting-based approach that inverts the original Gaussian splatting formulation: instead of rendering a single scene from multiple cameras, we render multiple object instances from a single camera. Our method is initialized with SAM3 instance segmentation masks and a modified Structure from Motion (SfM) pipeline. In contrast to learned monocular depth estimation, we perform true geometry-based reconstruction from image evidence, avoiding hallucinations caused by training data priors. We evaluate MooMIns on synthetic and real bin-picking scenarios, and demonstrate accurate reconstruction of previously unseen objects as well as reliable pose estimation of individual instance
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