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).

翻译：本文提出GaussianOcc，一种系统性方法，探索高斯泼溅在环视场景中实现全自监督高效三维占据栅格估计的两种应用路径。首先，传统自监督三维占据栅格估计方法在训练阶段仍需依赖传感器提供的真实六自由度位姿。为突破此限制，我们提出投影式高斯泼溅模块，通过相邻视图投影为全自监督训练提供精确尺度信息。此外，现有方法依赖体渲染技术，利用二维信号（深度图、语义图）进行最终三维体素表征学习，该方法耗时且效率有限。我们提出体素空间高斯泼溅模块，充分发挥高斯泼溅的快速渲染特性。最终，所提GaussianOcc方法实现了全自监督（无需真实位姿）的三维占据栅格估计，在保持竞争性性能的同时显著降低计算成本（训练速度提升2.7倍，渲染速度提升5倍）。