用高斯表示3D场景，不存图像也能高效定位，内存降94倍。
GEVO: Memory-Efficient Monocular Visual Odometry Using Gaussians
- 不存历史图像，通过现有地图实时渲染重建画面
- 地图精度与现有方法相当，内存仅约58MB
- 适合手机、AR/VR等内存受限设备使用
单目相机构建高保真3D场景可支持微机器人、智能手机及AR/VR头显等多种移动应用。在这些设备上，内存容量有限，访问内存常主导计算能耗。尽管高斯点阵（Gaussian Splatting, GS）能实现高质量3D重建，但现有基于GS的SLAM因需存储大量历史图像以重训练高斯点避免灾难性遗忘，导致内存占用高达地图本身两数量级，成为主要瓶颈。本文提出GEVO，一种基于高斯的单目视觉里程计框架，通过从现有地图中实时渲染而非存储历史图像，实现与先前方法相当的重建质量。提出新颖的高斯初始化与优化策略，消除地图伪影并延缓渲染图像退化。在多种环境测试中，GEVO保持相近地图保真度的同时，内存开销降至约58MB，较之前工作降低最多达94倍。
原文摘要 · Abstract (English)
Constructing a high-fidelity representation of the 3D scene using a monocular camera can enable a wide range of applications on mobile devices, such as micro-robots, smartphones, and AR/VR headsets. On these devices, memory is often limited in capacity and its access often dominates the consumption of compute energy. Although Gaussian Splatting (GS) allows for high-fidelity reconstruction of 3D scenes, current GS-based SLAM is not memory efficient as a large number of past images is stored to retrain Gaussians for reducing catastrophic forgetting. These images often require two-orders-of-magnitude higher memory than the map itself and thus dominate the total memory usage. In this work, we present GEVO, a GS-based monocular SLAM framework that achieves comparable fidelity as prior methods by rendering (instead of storing) them from the existing map. Novel Gaussian initialization and optimization techniques are proposed to remove artifacts from the map and delay the degradation of the rendered images over time. Across a variety of environments, GEVO achieves comparable map fidelity while reducing the memory overhead to around 58 MBs, which is up to 94x lower than prior works.
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。