Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection
<|reference_start|>Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection: 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.<|reference_end|>
@article{yan2024gaussian-det:,
    title={Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection},
    author={Hongru Yan, Yu Zheng, Yueqi Duan},
    journal={arXiv preprint arXiv:2410.01404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01404},
    primaryClass={cs.CV}
}