论文标题
切碎：3D形状区域分解，并通过学习的本地操作
SHRED: 3D Shape Region Decomposition with Learned Local Operations
论文作者
论文摘要
我们提出切碎，这是一种3D形状区域分解的方法。 Shred将3D点云作为输入，并使用学习的本地操作来产生近似细粒零件实例的分割。我们将切碎的分解操作赋予了三个分解操作：分裂区域，固定区域之间的边界，并将区域合并在一起。模块进行了独立和本地培训，从而使切碎可以为训练期间未见的类别生成高质量的细分。我们训练和评估partnet细分细分的切碎；使用其合并 - 阈值超参数，我们表明，在任何所需的分解粒度下，切碎的分割可以更好地尊重与基线方法相比，可以更好地尊重地面真相的注释。最后，我们证明切碎对于下游应用非常有用，在零摄像的细粒零件实例分割上表现出所有基准，而当与学习标记形状区域的方法结合使用时，碎片碎片的细分零件实例分割和很少的细粒语义分割。
We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a 3D point cloud as input and uses learned local operations to produce a segmentation that approximates fine-grained part instances. We endow SHRED with three decomposition operations: splitting regions, fixing the boundaries between regions, and merging regions together. Modules are trained independently and locally, allowing SHRED to generate high-quality segmentations for categories not seen during training. We train and evaluate SHRED with fine-grained segmentations from PartNet; using its merge-threshold hyperparameter, we show that SHRED produces segmentations that better respect ground-truth annotations compared with baseline methods, at any desired decomposition granularity. Finally, we demonstrate that SHRED is useful for downstream applications, out-performing all baselines on zero-shot fine-grained part instance segmentation and few-shot fine-grained semantic segmentation when combined with methods that learn to label shape regions.
