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).
我们介绍了一种系统化方法——GaussianOcc，该方法研究了高斯喷涂在完全自监督和高效的环绕视角三维占用估计中的两种用法。首先，传统的自监督三维占用估计方法在训练期间仍需要传感器提供的真实6D位姿数据。为了解决这一局限性，我们提出了用于投影的高斯喷涂（GSP）模块，通过相邻视角投影提供准确的尺度信息，从而实现完全自监督的训练。此外，现有方法依赖于体渲染来使用二维信号（深度图、语义图）进行最终三维体素表示学习，这不仅耗时而且效果较差。我们提出了从体素空间进行高斯喷涂（GSV），以利用高斯喷涂的快速渲染特性。结果表明，提出的 GaussianOcc 方法在没有真实位姿的情况下实现了完全自监督的三维占用估计，并且在性能具有竞争力的同时，计算成本较低（训练速度提高了2.7倍，渲染速度提高了5倍）。