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.

翻译：包裹我们身体的皮肤、覆盖沙发的皮革、覆盖汽车的金属板——这些现象表明物体被一系列连续表面所包围，这为我们提供了用于物体性推断的信息化几何先验。本文提出高斯检测器，利用高斯泼溅作为基于多视图的三维物体检测的表面表示方法。与现有基于单目或神经辐射场的方法通过离散位置数据描述物体不同，高斯检测器以连续方式建模物体，将输入高斯函数表述为大量局部表面上的特征描述符。此外，为解决高斯泼溅固有引入的大量异常值，我们相应设计了闭合推断模块，用于实现基于表面的全面物体性推断。该模块首先针对高斯泼溅的不确定性本质估计局部表面的概率特征残差，随后将这些残差融合为物体提议整体表面闭合的完整表示。通过这种方式，高斯检测器所利用的表面信息既作为物体性质量与可靠性的先验依据，也作为提议优化的信息基础。在合成数据集和真实数据集上的实验表明，高斯检测器在平均精度和召回率方面均优于现有多种方法。