Weakly-supervised 3D occupancy perception is crucial for vision-based autonomous driving in outdoor environments. Previous methods based on NeRF often face a challenge in balancing the number of samples used. Too many samples can decrease efficiency, while too few can compromise accuracy, leading to variations in the mean Intersection over Union (mIoU) by 5-10 points. Furthermore, even with surrounding-view image inputs, only a single image is rendered from each viewpoint at any given moment. This limitation leads to duplicated predictions, which significantly impacts the practicality of the approach. However, this issue has largely been overlooked in existing research. To address this, we propose GSRender, which uses 3D Gaussian Splatting for weakly-supervised occupancy estimation, simplifying the sampling process. Additionally, we introduce the Ray Compensation module, which reduces duplicated predictions by compensating for features from adjacent frames. Finally, we redesign the dynamic loss to remove the influence of dynamic objects from adjacent frames. Extensive experiments show that our approach achieves SOTA results in RayIoU (+6.0), while also narrowing the gap with 3D- supervised methods. This work lays a solid foundation for weakly-supervised occupancy perception. The code is available at https://github.com/Jasper-sudo-Sun/GSRender.

翻译：弱监督3D占据感知对于户外环境中基于视觉的自动驾驶至关重要。先前基于NeRF的方法常常面临采样数量平衡的挑战：采样过多会降低效率，过少则会损害精度，导致平均交并比（mIoU）波动达5-10个点。此外，即使输入环视图像，在任意给定时刻每个视角仅渲染单张图像。这一限制导致重复预测，严重影响了方法的实用性。然而，该问题在现有研究中大多被忽视。为此，我们提出GSRender，利用3D高斯泼溅进行弱监督占据估计，从而简化采样过程。同时，我们引入光线补偿模块，通过补偿相邻帧的特征来减少重复预测。最后，我们重新设计了动态损失函数，以消除相邻帧中动态物体的影响。大量实验表明，我们的方法在RayIoU指标上取得了领先结果（提升+6.0），同时缩小了与3D全监督方法之间的差距。本工作为弱监督占据感知奠定了坚实基础。代码已发布于https://github.com/Jasper-sudo-Sun/GSRender。