In embodied vision, Instance ImageGoal Navigation (IIN) requires an agent to locate a specific object depicted in a goal image within an unexplored environment. The primary challenge of IIN arises from the need to recognize the target object across varying viewpoints while ignoring potential distractors. Existing map-based navigation methods typically use Bird's Eye View (BEV) maps, which lack detailed texture representation of a scene. Consequently, while BEV maps are effective for semantic-level visual navigation, they are struggling for instance-level tasks. To this end, we propose a new framework for IIN, Gaussian Splatting for Visual Navigation (GaussNav), which constructs a novel map representation based on 3D Gaussian Splatting (3DGS). The GaussNav framework enables the agent to memorize both the geometry and semantic information of the scene, as well as retain the textural features of objects. By matching renderings of similar objects with the target, the agent can accurately identify, ground, and navigate to the specified object. Our GaussNav framework demonstrates a significant performance improvement, with Success weighted by Path Length (SPL) increasing from 0.347 to 0.578 on the challenging Habitat-Matterport 3D (HM3D) dataset. The source code is publicly available at the link: https://github.com/XiaohanLei/GaussNav.

翻译：在具身视觉领域，实例图像目标导航（IIN）要求智能体在未探索环境中定位目标图像中描绘的特定物体。IIN的主要挑战在于需要从不同视角识别目标物体，同时忽略潜在的干扰物。现有的基于地图的导航方法通常使用鸟瞰图（BEV）地图，这类地图缺乏场景的详细纹理表征。因此，尽管BEV地图在语义级视觉导航中表现有效，但在实例级任务中却面临困难。为此，我们提出了一种新的IIN框架——基于高斯泼溅的视觉导航（GaussNav），该框架基于3D高斯泼溅（3DGS）构建了一种新颖的地图表征。GaussNav框架使智能体能够同时记忆场景的几何与语义信息，并保留物体的纹理特征。通过将相似物体的渲染图像与目标进行匹配，智能体能够准确识别、定位并导航至指定物体。我们的GaussNav框架在具有挑战性的Habitat-Matterport 3D（HM3D）数据集上实现了显著的性能提升，路径长度加权成功率（SPL）从0.347提高至0.578。源代码已通过以下链接公开：https://github.com/XiaohanLei/GaussNav。