We present a novel framework for finding a set of tight bounding boxes of a
3D shape via neural-network-based over-segmentation and iterative merging and
refinement. Achieving tight bounding boxes of a shape while guaranteeing the
complete boundness is an essential task for efficient geometric operations and
unsupervised semantic part detection, but previous methods fail to achieve both
full coverage and tightness. Neural-network-based methods are not suitable for
these goals due to the non-differentiability of the objective, and also classic
iterative search methods suffer from their sensitivity to the initialization.
We demonstrate that the best integration of the learning-based and iterative
search methods can achieve the bounding boxes with both properties. We employ
an existing unsupervised segmentation network to split the shape and obtain
over-segmentation. Then, we apply hierarchical merging with our novel
tightness-aware merging and stopping criteria. To overcome the sensitivity to
the initialization, we also refine the bounding box parameters in a game setup
with a soft reward function promoting a wider exploration. Lastly, we further
improve the bounding boxes with a MCTS-based multi-action space exploration.
Our experimental results demonstrate the full coverage, tightness, and the
adequate number of bounding boxes of our method.

本研究提出了一种基于神经网络的过分割和迭代合并和细化的新框架，以找到 3D 形状的一组紧密边界框。通过使用现有的无监督分割网络分割形状并获得过分割，应用具有紧密感知合并和停止标准的分层合并。最后，通过基于 MCTS 的多行动探索进一步改进界限框。

通过学习超分割和迭代搜索来拟合紧密边界框的分离、合并和优化

Split, Merge, and Refine: Fitting Tight Bounding Boxes via Learned  Over-Segmentation and Iterative Search

In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in the training data. So the models should be inherently robust and
adaptive to occlusions instead of fitting / learning the occlusion patterns in
the training data. Our approach detects semantic parts by accumulating the
confidence of local visual cues. Specifically, the method uses a simple voting
method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are
derived by clustering the internal states of deep networks. We evaluate our
voting scheme on the VehicleSemanticPart dataset with dense part annotations.
We randomly place two, three or four irrelevant objects onto the target object
to generate testing images with various occlusions. Experiments show that our
algorithm outperforms several competitors in semantic part detection when
occlusions are present.

本篇论文介绍了一种用于检测部分遮挡对象的语义部分的方法，该方法使用本地视觉线索的置信度进行语义部分的检测并使用简单的投票技术来处理局部线索。实验证明，该方法在存在遮挡的情况下，在语义部分检测方面性能优于其他竞争方法。