3D-DLP通过自监督学习将场景分解为可操控的3D粒子，实现物体级语义理解。
3D-DLP: Self-Supervised 3D Object-Centric Scene Representation Learning
- 将RGB-D或体素输入分解为带属性的3D潜粒子，每粒子对应一个独立物体
- 在模拟与真实数据上均实现可解释的粒子分割与新场景生成能力
- 适用于机器人抓取等任务，相比基线提升性能且节省内存
我们提出3D-DLP，一种基于自监督的物体中心表征学习模型，能将场景级的RGB-D或体素观测分解为一组3D潜粒子。该模型基于深度潜粒子（DLP）框架，每个粒子编码解耦的属性，包括3D关键点位置、边界框尺寸和外观特征，并代表场景中的一个独立实体。通过端到端的自监督重建目标，模型学习可解释的逐粒子分割图。我们在模拟和真实世界数据集上验证了所学潜空间的可解释性与可控性：通过操纵粒子位置并解码，可生成新场景配置。此外，利用这些紧凑的3D潜粒子进行下游机器人操作任务，性能优于缺乏显式3D信息或依赖高内存密集3D输入但无物体中心结构的基线方法。代码与视频见https://eubooks3003.github.io/3d-dlp。
原文摘要 · Abstract (English)
We introduce 3D-DLP, a self-supervised object-centric representation learning model that decomposes scene-level RGB-D or voxel observations into a set of 3D latent particles. Building on the Deep Latent Particles (DLP) framework, each particle encodes disentangled attributes, including 3D keypoint position, bounding box dimensions, and appearance features, and represents a distinct entity in the scene. The model learns interpretable per-particle segmentation maps through an end-to-end self-supervised reconstruction objective. We demonstrate on both simulated and real-world datasets that the learned latent space is interpretable and controllable: by manipulating particle positions and decoding, we can generate novel scene configurations. Furthermore, we show that leveraging these compact 3D latent particles for downstream robotic manipulation improves performance over baselines that either lack explicit 3D information or rely on memory-intensive dense 3D inputs without object-centric structure. Code and videos are available at https://eubooks3003.github.io/3d-dlp.
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