用稀疏高斯点代替体素，实现高效弱监督场景占据估计
GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow
- 用高斯点云替代密集体素，结合时序流估计动态变化
- 在nuScenes上性能超越现有方法，推理速度提升50倍
- 无需昂贵标注，适合自动驾驶等实际场景部署
占据估计已成为3D计算机视觉中的重要任务，尤其在自动驾驶领域。本文提出GaussianFlowOcc，一种基于高斯点云的稀疏3D表示方法，替代传统密集体素网格。通过高斯变压器架构，模型避免了耗时的3D卷积，显著降低计算与内存开销。该方法在训练过程中为每个高斯点估计时序流动，有效捕捉场景动态变化，解决了以往方法常忽略的问题。同时，模型采用弱监督策略，无需依赖额外数据（如LiDAR）进行密集3D体素标注，具备良好可扩展性。大量实验表明，GaussianFlowOcc在nuScenes数据集上显著优于此前所有弱监督占据估计方法，且推理速度达当前SOTA的50倍。
原文摘要 · Abstract (English)
Occupancy estimation has become a prominent task in 3D computer vision, particularly within the autonomous driving community. In this paper, we present a novel approach to occupancy estimation, termed GaussianFlowOcc, which is inspired by Gaussian Splatting and replaces traditional dense voxel grids with a sparse 3D Gaussian representation. Our efficient model architecture based on a Gaussian Transformer significantly reduces computational and memory requirements by eliminating the need for expensive 3D convolutions used with inefficient voxel-based representations that predominantly represent empty 3D spaces. GaussianFlowOcc effectively captures scene dynamics by estimating temporal flow for each Gaussian during the overall network training process, offering a straightforward solution to a complex problem that is often neglected by existing methods. Moreover, GaussianFlowOcc is designed for scalability, as it employs weak supervision and does not require costly dense 3D voxel annotations based on additional data (e.g., LiDAR). Through extensive experimentation, we demonstrate that GaussianFlowOcc significantly outperforms all previous methods for weakly supervised occupancy estimation on the nuScenes dataset while featuring an inference speed that is 50 times faster than current SOTA.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。