We introduce GaussianOcc, a systematic method that investigates the two usages of Gaussian splatting for fully self-supervised and efficient 3D occupancy estimation in surround views. First, traditional methods for self-supervised 3D occupancy estimation still require ground truth 6D poses from sensors during training. To address this limitation, we propose Gaussian Splatting for Projection (GSP) module to provide accurate scale information for fully self-supervised training from adjacent view projection. Additionally, existing methods rely on volume rendering for final 3D voxel representation learning using 2D signals (depth maps, semantic maps), which is both time-consuming and less effective. We propose Gaussian Splatting from Voxel space (GSV) to leverage the fast rendering properties of Gaussian splatting. As a result, the proposed GaussianOcc method enables fully self-supervised (no ground truth pose) 3D occupancy estimation in competitive performance with low computational cost (2.7 times faster in training and 5 times faster in rendering). The relevant code is available in https://github.com/GANWANSHUI/GaussianOcc.git.

翻译：我们提出了GaussianOcc，这是一种系统性的方法，旨在探索高斯溅射在环视场景中实现全自监督高效三维占据估计的两种应用方式。首先，传统的自监督三维占据估计方法在训练过程中仍然需要来自传感器的真实6D位姿。为了解决这一限制，我们提出了用于投影的高斯溅射模块，通过相邻视图投影为全自监督训练提供精确的尺度信息。此外，现有方法依赖于体渲染，利用二维信号（深度图、语义图）进行最终的三维体素表示学习，这种方法既耗时又效率较低。我们提出了从体素空间出发的高斯溅射方法，以利用高斯溅射的快速渲染特性。因此，所提出的GaussianOcc方法能够以较低的计算成本（训练速度快2.7倍，渲染速度快5倍）实现具有竞争力的全自监督（无需真实位姿）三维占据估计。相关代码可在 https://github.com/GANWANSHUI/GaussianOcc.git 获取。