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Title: Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories

Paper content:
We address the problem of learning to discover 3D parts for objects in unseen categories.
Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches.
Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion.
At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories.
On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples.
Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.
Perceptual grouping has been a long-standing problem in the study of vision systems (Hoffman & Richards, 1984) .
The process of perceptual grouping determines which regions of the visual input belong together as parts of higher-order perceptual units.
Back to the 1930s, Wertheimer (1938) listed several vital factors, such as similarity, proximity, and good continuation, which lead to visual grouping.
To this era of deep learning, grouping cues can be learned from massive annotated datasets.
However, compared with human visual system, these learning-based segmentation algorithms are far inferior for objects from unknown categories.
We are interested in attacking a specific problem of this kind -zero-shot part discovery for 3D shapes.
We choose to study the zero-shot learning problem on 3D shape data instead of 2D image data, because part-level similarity across object categories in 3D is more salient and less affected by various distortions introduced in the imaging process.
Work done while Tiange Luo, Kaichun Mo, Jiarui Xu, and Siyu Hu were visiting UC San Diego.
To motive our approach, we first review the key idea and limitation of existing 3D part segmentation methods.
With the power of big data, deep neural networks that learn data-driven features to segment shape parts, such as (Kalogerakis et al., 2010; Graham et al., 2018; Mo et al., 2019c) , have demonstrated the state-of-the-art performance on many shape segmentation benchmarks (Yi et al., 2016; Mo et al., 2019c) .
These networks usually have large receptive fields that cover the whole input shape, so that global context can be leveraged to improve the recognition of part semantics and shape structures.
While learning such features leads to superior performance on the training categories, they often fail miserably on unseen categories (Figure 1 ) due to the difference of global shapes.
On the contrary, classical shape segmentation methods, such as (Kaick et al., 2014 ) that use manually designed features with relatively local context, can often perform much better on unseen object categories, although they tend to give inferior segmentation results on training categories.
In fact, many globally different shapes share similar part-level structures.
For example, airplanes, cars, and swivel chairs all have wheels, even though their global geometries are totally different.
Having learned the geometry of wheels from airplanes should help recognize wheels for cars and swivel chairs.
In this paper, we aim to invent a learning-based framework that will by design avoid using excessive context information that hurts cross-category generalization.
We start from learning to propose a pool of superpixel-like sub-parts for each shape.
Then, we learn a grouping policy that seeks to progressively group sub-parts and increase recognition context.
What lies in the heart of our algorithm is to learn a function to assess whether two parts should be grouped.
Different from prior deep segmentation work that learns point features for segmentation mask prediction, our formulation essentially learns part-level features.
Borrowing ideas from Reinforcement Learning (RL), we formalize the process as a contextual bandit problem and train a local grouping policy to iteratively pick a pair of most promising sub-parts for grouping.
In this way, we restrict that our features only convey information within the local context of a part.
Our learning-based agglomerative clustering framework deviates drastically from the prevailing deep segmentation pipelines and makes one step towards generalizable part discovery in unseen object categories.
To summarize, we make the following contributions:
• We formulate the task of zero-shot part discovery on a large-scale fine-grained shape segmentation benchmark PartNet (Mo et al., 2019c ); • We propose a learning-based agglomerative clustering framework that learns to do part proposals and grouping from training categories and generalizes to unseen novel categories; • We quantitatively compare our approach to several baseline methods and demonstrate the state-of-the-art results for part discovery in unseen object categories.
In this paper, we introduced a data-driven iterative perceptual grouping pipeline for the task of zero-shot 3D shape part discovery.
At the core of our method is to learn part-level features within part local contexts, in order to generalize the part discovery process to unseen novel categories.
We conducted extensive evaluation and analysis of our method and presented thorough quantitative comparisons to four state-of-the-art shape segmentation algorithms.
We demonstrated that our method successfully extracts locally-aware part knowledge from training categories and transfers the knowledge to unseen novel categories.
Our method achieved the best performance over all four baseline methods on the PartNet dataset.
A SUB-PART PROPOSAL MODULE Given a shape represented as a point cloud, we first propose a pool of small superpixel-like (Ren & Malik, 2003) sub-parts as the building blocks.
We employ furthest point sampling to sample 128 seed points on each input shape.
To capture the local part context, we extract PointNet (Qi et al., 2017a) features with 64 points sampling within a local 0.04-radius 2 neighborhood around each seed point.
In the training phase, all the 64 points will be sampled from the same instance.
Then, we train a local PointNet segmentation network that takes as inputs 512 points within a 0.2-radius ball around every seed point and output a binary segmentation mask indicating a sub-part proposal.
If the point belongs to the instance is the same as the 0.04-radius ball, it will be classified into 1.
We call this module as the sub-part proposal module and illustrate it in Figure 4 .
In the inference phase, we can not guarantee the 64 points sampled within a 0.04-radius ball are all coming from the same part.
However, in our experiments, we observe those sub-part proposals will have a low purity score due to the poor center feature extracted from the 64 points across different parts.
Also, even the center feature extraction is good, some sub-parts may also cover multiple parts in ground-truth.
To obtain high-quality sub-parts, we remove the sub-parts whose purity score lower than 0.8, and the remain sub-parts form our initial sub-part pool.
The input of this learning module is constrained in a local region, thus will not be affected by the global context.
To validate the transferring performance of this module, we train the module on Chair, Storage Furniture, and Lamp of level-3 annotations and test on all categories with evaluating by the most fine-grained level annotations of each category.
The results are listed in Table 3 .
Since the part patterns in Table 3 : Quantitative evaluation of the sub-part proposal module.
PosAcc and NegAcc refer to positive accuracy and negative accuracy of the binary segmentation.

assistant: text: A zero-shot segmentation framework for 3D object part segmentation. Model the segmentation as a decision-making process and solve as a contextual bandit problem.