CODA：从单张RGB-D图像进行深度对齐的场景补全与物体分解
CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image
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中文总结 AI 辅助
CODA提出从单张RGB-D图像先补全完整场景几何再分解物体的生成模型，通过两种3D接地机制减少漂移，在杂乱场景数据集上实现更准确重建和更高物体稳定性。
中文摘要 AI 辅助
在杂乱日常环境中安全运行的机器人通常需要从部分观测中推断场景几何结构。在2D中检测物体并独立重建它们的方法在此类场景中会遇到困难：漏检的物体永远不会被重建，合并的检测可能融合两个物体，而单独重建的网格可能重叠或无法接触其支撑表面。我们提出了CODA（Complete Once, Decompose Afterward，先补全后分解），这是一种生成模型，它从单张未分割的RGB-D图像重建完整的场景几何结构，然后将表面分离为周围环境和可移动物体。然而，生成的场景几何结构可能偏离观测到的部分点云。为减少这种漂移，CODA使用两种显式3D接地机制，在补全未见区域的同时，使重建的几何结构与观测表面保持一致。在HomebrewedDB和我们自建的杂乱场景数据集上的实验表明，与先物体后场景及先场景后物体的基线方法相比，CODA实现了更准确的重建，并且在模拟重力下保持原位物体的比例更高。
英文摘要
Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects in 2D and reconstruct them independently struggle in such scenes: a missed object is never reconstructed, a merged detection can fuse two objects, and separately reconstructed meshes may overlap or fail to touch their supporting surfaces. We introduce CODA (Complete Once, Decompose Afterward), a generative model that instead reconstructs the complete scene geometry from a single unsegmented RGB-D image, then separates the surface into the surrounding environment and movable objects. Still, generated scene geometry can drift from the observed partial point cloud. To reduce this drift, CODA uses two explicit 3D grounding mechanisms to keep reconstructed geometry consistent with observed surfaces while completing unseen regions. Experiments on HomebrewedDB and our custom cluttered-scene dataset show more accurate reconstructions and a higher fraction of objects remaining in place under simulated gravity than both object-first and scene-first baselines.
发表机构
- KAIST（韩国科学技术院）
- Samsung Heavy Industries Co., Ltd.（三星重工有限公司）
机构由 AI 辅助整理，请以论文原文为准。
