We present CARTO, a novel approach for reconstructing multiple articulated
objects from a single stereo RGB observation. We use implicit object-centric
representations and learn a single geometry and articulation decoder for
multiple object categories. Despite training on multiple categories, our
decoder achieves a comparable reconstruction accuracy to methods that train
bespoke decoders separately for each category. Combined with our stereo image
encoder we infer the 3D shape, 6D pose, size, joint type, and the joint state
of multiple unknown objects in a single forward pass. Our method achieves a
20.4% absolute improvement in mAP 3D IOU50 for novel instances when compared to
a two-stage pipeline. Inference time is fast and can run on a NVIDIA TITAN XP
GPU at 1 HZ for eight or less objects present. While only trained on simulated
data, CARTO transfers to real-world object instances. Code and evaluation data
is available at: this http URL

使用隐式物体中心表示法和编码器 / 解码器，提出了一种重建多个关节物体的方法，该方法可以在单个观察中推断多个未知物体的 3D 形状，6D 姿态，类型和状态，并且在虚拟和现实世界中都得到了很好的表现。