Skins wrapping around our bodies, leathers covering over the sofa, sheet metal coating the car - it suggests that objects are enclosed by a series of continuous surfaces, which provides us with informative geometry prior for objectness deduction. In this paper, we propose Gaussian-Det which leverages Gaussian Splatting as surface representation for multi-view based 3D object detection. Unlike existing monocular or NeRF-based methods which depict the objects via discrete positional data, Gaussian-Det models the objects in a continuous manner by formulating the input Gaussians as feature descriptors on a mass of partial surfaces. Furthermore, to address the numerous outliers inherently introduced by Gaussian splatting, we accordingly devise a Closure Inferring Module (CIM) for the comprehensive surface-based objectness deduction. CIM firstly estimates the probabilistic feature residuals for partial surfaces given the underdetermined nature of Gaussian Splatting, which are then coalesced into a holistic representation on the overall surface closure of the object proposal. In this way, the surface information Gaussian-Det exploits serves as the prior on the quality and reliability of objectness and the information basis of proposal refinement. Experiments on both synthetic and real-world datasets demonstrate that Gaussian-Det outperforms various existing approaches, in terms of both average precision and recall.
我们的皮肤包裹着身体，皮革覆盖着沙发，金属板覆盖着汽车——这些现象表明物体被一系列连续的表面所包围，这为物体性推断提供了有用的几何先验信息。在本文中，我们提出了Gaussian-Det，它利用高斯散射作为基于多视图的3D目标检测的表面表示。与现有的基于单目或NeRF的方法使用离散位置数据来描述物体不同，Gaussian-Det通过将输入的高斯函数表述为部分表面的特征描述符，以连续的方式建模物体。此外，为了解决高斯散射本质上引入的大量离群点问题，我们相应地设计了一个封闭推理模块（Closure Inferring Module, CIM），用于全面的基于表面的物体性推断。CIM首先根据高斯散射的欠确定性估计部分表面的概率特征残差，随后将其整合成物体提案整体表面的封闭性表示。通过这种方式，Gaussian-Det利用的表面信息作为物体性质量和可靠性的先验，同时也是提案精炼的信息基础。在合成数据集和真实世界数据集上的实验表明，Gaussian-Det在平均精度和召回率方面均优于多种现有方法。