ECHO: Embodied Camera Observations of Human Object Carrying
作者: Xuefei Sun, Lorin Achey, Kali Hamilton, Alberto Speranzon, Gregory Grebe, Yonatan Bisk, Christoffer Heckman
分类: cs.CV, cs.RO
发布日期: 2026-10-07
💡 一句话要点
提出ECHO数据集以解决人类物体携带场景理解问题
🎯 匹配领域: 支柱五：交互与反应 (Interaction & Reaction)
关键词: 上下文推理 物体放置 多模态融合 RGB-D扫描 人类活动理解 数据集构建 具身智能体
📋 核心要点
- 现有方法在理解人类物体携带行为时缺乏有效的基准和数据集，无法同时捕捉场景重建与人类活动。
- 本文提出ECHO数据集，结合RGB-D扫描与人类携带物体的记录，定义上下文物体放置任务以进行评估。
- 实验结果表明，单一输入模态无法满足需求，强调了多模态信息联合推理的重要性。
📝 摘要（中文）
具身和辅助智能体不仅需要识别物体，还需推理物体在环境中的归属及人类习惯。现有RGB-D扫描数据集无法有效捕捉人类活动，而人类物体交互数据集缺乏完整场景重建。为此，本文提出了上下文物体放置基准任务，并引入ECHO数据集，结合室内场景的RGB-D扫描与人类携带物体的记录。ECHO是首个公开的数据集，包含重建场景、人类活动、自然语言及上下文放置注释，涵盖3,805个标注案例，涉及198种物体，提供丰富的同步数据，评估结果显示，单一输入模态不足以解决问题，强调了场景结构、人类活动及上下文知识的联合推理需求。
🔬 方法详解
问题定义：本文旨在解决具身智能体在理解人类物体携带行为时的上下文推理问题。现有方法缺乏专门的数据集，无法有效捕捉人类活动与环境布局的关系。
核心思路：论文提出ECHO数据集，结合室内场景的RGB-D扫描与人类携带物体的记录，定义上下文物体放置任务，旨在通过多模态信息提升推理能力。
技术框架：ECHO数据集包含多个模块，包括完整的RGB-D场景重建、标注的人类活动轨迹、物体携带的起始与目标表面、动作描述及上下文信息，形成一个综合的评估平台。
关键创新：ECHO是首个结合重建场景、人类活动和上下文注释的公开数据集，填补了现有数据集的空白，提供了丰富的上下文信息以支持物体放置推理。
关键设计：数据集中包含3,805个标注案例，198种物体，提供同步的RGB-D片段、6自由度的相机与物体轨迹等，设计上注重多模态信息的整合与利用。
🖼️ 关键图片
📊 实验亮点
实验结果显示，ECHO数据集在上下文物体放置任务中表现出色，单一输入模态的性能不足以满足需求，强调了多模态信息的联合推理。具体性能数据未提供，需进一步研究验证。
🎯 应用场景
ECHO数据集的提出为具身智能体在复杂环境中的物体放置推理提供了重要的数据支持，具有广泛的应用潜力，包括家庭助理、机器人导航及人机交互等领域。未来，随着数据集的进一步扩展和应用，可能推动智能体在动态环境中的自主决策能力提升。
📄 摘要（原文）
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people who live in it. Progress on this problem has been limited, in part because no dedicated benchmark or dataset exists to define and evaluate it. Existing RGB-D scan datasets reconstruct static rooms without human activity, while human-object-interaction datasets capture motion without a navigable, fully reconstructed scene or a ground-truth notion of an object's natural destination. We introduce contextual object placement as a benchmark task: predicting an object's destination during an observed object-carrying episode. To support this task, we present Embodied Camera observations of Human Object carrying (ECHO), a large-scale synthetic dataset that pairs dense RGB-D scans of indoor scenes with recordings of an embodied human carrying everyday objects to context-appropriate destinations. ECHO is the first publicly available dataset to combine reconstructed scenes, human activity, natural language, and contextual-placement annotations. It comprises 3,805 human-annotated episodes across 159 floors of 115 HM3D scenes, involving 198 distinct objects. Each floor includes a complete RGB-D scan with human-annotated room labels and a surface list. Each episode provides synchronized RGB-D encounter clips; 6-DoF camera, human, and object trajectories; start and destination surfaces; an action caption; and a human-written context: a single sentence describing the inhabitant's routine that implies the destination without naming it. We evaluate contextual object placement using input-masked probes and an end-to-end baseline. Results show that no single input modality is sufficient, highlighting the need to jointly reason over scene structure, human activity, and contextual knowledge.