GaussianFlowOcc：使用高斯溅射和时间流的稀疏弱监督占用估计
GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow
AI总结：
针对3D占用估计任务，提出GaussianFlowOcc方法：以稀疏3D高斯替代密集体素，通过高斯Transformer架构降本增效，结合时间流捕捉动态，支持弱监督；在nuScenes上性能超现有方法，推理速度快50倍。
摘要：
占用估计已成为3D计算机视觉中的一项重要任务，尤其在自动驾驶领域。本文提出一种名为GaussianFlowOcc的新型占用估计方法，其灵感来源于高斯溅射（Gaussian Splatting），并用稀疏3D高斯表示替代传统密集体素网格。我们基于高斯Transformer的高效模型架构，通过消除低效体素表示所需的昂贵3D卷积（此类表示主要描述空3D空间），大幅降低了计算和内存需求。GaussianFlowOcc在整个网络训练过程中通过估计每个高斯的时间流有效捕捉场景动态，为现有方法常忽略的复杂问题提供了简洁解决方案。此外，GaussianFlowOcc支持弱监督且无需基于额外数据（如LiDAR）的昂贵密集3D体素标注，具有可扩展性。大量实验表明，在nuScenes数据集的弱监督占用估计任务中，GaussianFlowOcc显著优于所有现有方法，且推理速度比当前SOTA快50倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
