Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes the new task of part-level affordance discovery: it is a joint task of decomposing 3D shapes into their parts and predicting how each part corresponds to affordances. A learning framework has been proposed for this task. It learns to segment 3D shapes into parts from weak shape-level labels. It also associates affordances with predicted parts. The framework is powered by 3D position-embedded features and 3D slot attention. To facilitate this new task, a novel dataset that features part-level cross-category 3D object affordances is constructed. Extensive evaluation and ablation studies have been performed on the dataset.
strength_and_weaknesses: ### Strength
- The new task, PartAfford, is interesting and I believe will promote new research in this direction.
- The new dataset is also a valuable contribution to the community. I especially like the openable affordance shapes (Figure 3c).
- The proposed method to discover 3D parts and associate affordance in a weakly supervised setting is interesting. 
- I like Section 6.4 which evaluates the generalization power of the proposed method on unseen objects.


### Weakness
- Although the 3D part affordance dataset contains 24 affordance labels, the experiment section only considers three: 'sittable', 'support', and 'openable'. I understand that they are very useful functions for everyday objects but I wish the method is evaluated on more categories for a comprehensive understanding of the task.

- I know the paper proposes a new task. But I think it could compare its individual components to other methods mentioned in the related work. For example, (unsupervised) 3D part discovery work could be compared. Also, even a fully supervised dense affordance learning scheme can be compared as a reference too because it is common to have such a method in the evaluation as the performance upper bound reference.

- Section 6.1 mentions that the current setting only allows one affordance per part. I think quite frequently an object part can afford multiple functions. It is not clear to me if the method is able to handle multiple affordance per part. 

- Comparing to Table 1, Table 2 doesn't have results of Slot MLP and IODINE. A complete table will be better.

clarity,_quality,_novelty_and_reproducibility: ### Clarity
The paper is straightforward and easy to follow. Some editing suggestions:
- Figure 1 left: I think **sparse** is confusing here. How about "weak"? 
- Figure 2 (d): the illustration of overlapping cuboids and parts is very confusing. It looks like that is another reconstruction while it is an intuitive illustration of the loss.

### Quality
The quality of the work is good.

### Novelty
The 3D part affordance dataset is novel. The proposed workflow contains components from prior work such as the 3D feature and position embedding and 3D slot attention mechanisms but the combination of everything to solve this new task is novel.

### Reproducibility
There seems to be a sufficient amount of detail in the paper for reproducibility. The author will release the code and data.
summary_of_the_review: Overall, I like this paper as it defines an interesting yet challenging task, i.e., jointly finding 3D parts and their affordance, and provides a viable solution. The new 3D part affordance dataset is also a nice contribution; it will promote more interesting research in this direction. There are a few places the paper could be improved as mentioned in the "Weakness" above. But I think the paper will be a good contribution to ICLR 2023 in its current form.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper studies the problem of discovering part affordances. This is an important topic in many applications. Previous methods focused on learning object affordances with dense supervision. In this work, the paper proposes a new task of part affordance discovery. This task is interesting and challenging The paper constructs a part-level, cross-category 3D object affordance dataset with 24 affordance categories shared among 25,000 objects.
strength_and_weaknesses: Strengths
1. The proposed task is interesting yet challenging and has value for many research communities.
2. Code is provided.
3. The video is very clear.

Weaknesses
1. Given that this paper is really interesting, it is very important to have a section that talks about limitations and future work in other problem domains, e.g., robotics.
2. I would also suggest that in camera ready it would be valuable to create a website that has an interactive viewer for people to view the dataset. 
clarity,_quality,_novelty_and_reproducibility: The paper reads well. The presentation quality is decent. Novelty is great. Code is provided and hence reproducibility is guaranteed.
summary_of_the_review: Please see the comments in the two boxes above.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a large-scale 3D object affordance learning and part discovery dataset, paired with a baseline method. The proposed task is important for 3D object understanding and would be impactful in many fields. The dataset consists of more than 25,000 objects and each object has a set of affordance labels. The objects mainly come from PartNet and PartNet-Mobility. The task requires the machine to discover the parts of these human-made objects and classify their affordance under a weakly-supervised learning setting. The proposed method uses slot attention as the main component with a multi-task head design to address this task. In experiments, this method achieves decent performance compared with some basic methods.
strength_and_weaknesses: Pros:
+ The aim and motivation of the benchmarks are sound and well-defined to make the community move on.

+ The dataset is large-scale and useful as a good complementary to PartNet.

+ The proposed method follows a sound design and uses slot attention as the feature bottleneck to finish three sub-tasks. And its performance looks well.

+ Code and data licenses are available.

Cons:
- The classes of affordance are limited. Even the dataset is large with a lot of objects. Furthermore, in tests, only several affordances are tested.

- Though there are analyses about the mutual effect of the three tasks, deeper insight is lacking to guide future study. I suggest a more profound discussion about the possible design and methods for future study, as this is a benchmark paper.

- Lacking a longtail distribution data analysis, e.g., affordances, object classes, parts, effect on performance, etc.

- More visualizations according to the above analysis would make this paper more solid.

- "We only keep the most prioritized affordance for each part to ease the ambiguities in learning", is somehow oversimplified.

- Possibilities scaling to some real scanned objects and more diverse objects?

- typo: in robotics (Nagarajan & Grauman, 2020; Mo et al., 2022).Prior --> ). Prior
clarity,_quality,_novelty_and_reproducibility: Clarity, well, easy-to-follow

Quality, well, more figures would be better

Novelty, meaningful annotation upon PartNet

Reproducibility, code, and data are provided, but I did not check all of them in the supplementary carefully
summary_of_the_review: Overall, this is an interesting paper with a lot of data, sound methods and experiment designs, and good writing. Though there is some room to improve (listed above), I think it is OK to be accepted.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The work focuses on learning object affordance. The authors present a new task called part-level affordance discovery (PartAfford). Given only the affordance labels for each object, the machine is tasked to (i) decompose 3D shapes into parts and (ii) discover how each part of the object corresponds to a certain affordance category. The authors propose a slot-attention-based framework with 3D part reconstruction, affordance prediction, and cuboidal primitive regularization. They construct a part affordance dataset and benchmark their method with several baselines they designed. 
strength_and_weaknesses: Strength

1. The paper studies object affordance, a critical problem with various applications such as scene understanding, object understanding, and object manipulation. Toward the goal, a part-level affordance discovery task is proposed. Compared to the existing densely-labeled paradigm, the proposed task offers a new challenge to the community that studies affordance detection, weakly-supervised segmentation, and part segmentation. Note that the authors propose a part affordance dataset with 24 affordance categories shared among > 25, 000 objects.
2. Figure 5 demonstrates the effectiveness of the proposed components. In particular, the authors successfully demonstrate that the proposed task cannot be tackled simply with the slot-attention algorithm (Locatello et al., 2020). In addition, the effectiveness of the cuboidal primitive regularization branch can be observed in Figure 5 (c).

Weaknesses

1. Task formulation: The authors motivate the readers that the work aims to answer a question: what is the natural way to learn object affordance from geometry with humanlike sparse supervision? Then, the authors answer the question from a labeling perspective, i.e., use part-level affordance instead a densely-labeled strategy. It makes sense to reduce the annotation efforts by proposing the task. However, the reviewer is not convinced that the task tries to answer the question directly. Specifically, other affordance-related works (e.g., Kjellström et al., CVIU 2011 and Nagarajan et al., ICCV 2019, Wang et al., ECCV 2022) that study affordance from an interaction perspective. With a detailed discussion with them, the reviewer has a hard time appreciating the value of the work.
    1. Kjellström et al., Visual object-action recognition: Inferring object affordances from human demonstration, CVIU 115 (2011) 81-90
    2. Nagarajan et al., Grounded Human-Object Interaction Hotspots from Video, ICCV 2019
    3. Wang et al., “AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions,” ECCV 2022
2. Part affordance dataset: the authors did not recognize the existing dataset (i.e., Myers et al., 2015). Note that they also consider affordance detection in cluttered environments. The authors should discuss the difference and why the dataset Myers et al., 2015 built cannot serve the need for evaluation. It is worth noting that the number of affordance categories in the proposed dataset is larger than Myers et al., 2015. However, the authors only conduct experiments on seven affordances. The experiments weaken the advantage of the proposed dataset.
    1. Myers et al., "Affordance Detection of Tool Parts from Geometric Features," ICRA 2015
3. The proposed algorithm is highly relevant to 3D weakly supervised semantic segmentation with scene-level labels (Wei et al., CVPR 2020 and Ren et al., CVPR 2021). They could serve as the baseline for the proposed task. The authors should justify why these methods are not considered in the experiments. Please comment.
    1. Wei et al., Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds, CVPR 2020
    2. Ren et al., 3D Spatial Recognition without Spatially Labeled 3D, CVPR 2021
4. Limited quantitative evaluation: The part affordance dataset has various objects and affordance categories. However, the authors only report a subset (i.e., sittable, support, and openable) in Tables 1 and 2. In addition, the paper also reports similar qualitative analysis on similar objects. Currently, the experimental results are insufficient to justify the proposed method's effectiveness in a wide range of tasks.
clarity,_quality,_novelty_and_reproducibility: 1. The motivation for the work is not clear. Specifically, is the proposed task more humanlike to learning object affordance? More details can be found in the Weakness section. 
2. The authors should justify why the existing weakly-supervised 3D semantic segmentation algorithms cannot be applied to the proposed task.
3. Currently, the experiments are insufficient to prove the importance of the dataset and proposed methods.
summary_of_the_review: Overall, the reviewer agrees that the proposed task is challenging. However, it is questionable to motivate the task from object affordance. In addition, the experimental section is not convincing. The reviewer looks forward to the author's feedback on the questions.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper presents a new task called part-level affordance discovery (PartAfford). Given only the affordance labels for each object, the method is tasked to (i) decompose 3D shapes into parts and (ii) discover how each part of the object corresponds to a certain affordance category. The authors propose a slot-attention-based framework with 3D part reconstruction, affordance prediction, and cuboidal primitive regularization. They construct a part affordance dataset with 24 affordance categories shared among > 25, 000 objects and benchmark their method with several baselines they designed. 

The reviewers raised concerns regarding the value of studying  affordance in this categorical labelling setting. Given that these labels, e.g., ``sittable”, do not provide any additional information on how to sit, what is the benefit of assigning this label in the first place, in place of chair seat, or car seat, or table, and then simply supply associate table, chair seat and car seat categories to sittable category.

The rebuttal clarifies that the main point of the paper is to alleviate dense labelling in favor of weak annotations. Then, it would be natural for the paper to compare against previous weakly supervised segmentation methods, and repurpose their model for their task. However the rebuttal explains why this repurposing is not possible.

The reviewers further raise concerns why not all labels are used for training affordance models, and the rebuttal shows experiments with expanded label sets. The rebuttal explains that  some labels have very few examples. 
Overall, the rebuttal notes: “Our work provides a stepping stone towards learning complex affordance from a weak supervision set.” but the proposed formulation remains unconvincing and the weakly supervised method proposed  is disconnected to previous weakly supervised methods and it appears hard to connect or re-use in a different setting. Thus, the paper is not suggested for publication in its current form. 



justification_for_why_not_higher_score: 

The reviewers raised concerns regarding the value of studying  affordance in this categorical labelling setting. Given that these labels, e.g., ``sittable”, do not provide any additional information on how to sit, what is the benefit of assigning this label in the first place, in place of chair seat, or car seat, or table, and then simply supply associate table, chair seat and car seat categories to sittable category.

The rebuttal clarifies that the main point of the paper is to alleviate dense labelling in favor of weak annotations. Then, it would be natural for the paper to compare against previous weakly supervised segmentation methods, and repurpose their model for their task. However the rebuttal explains why this repurposing is not possible.

The reviewers further raise concerns why not all labels are used for training affordance models, and the rebuttal shows experiments with expanded label sets. The rebuttal explains that  some labels have very few examples. 
Overall, the rebuttal notes: “Our work provides a stepping stone towards learning complex affordance from a weak supervision set.” but the proposed formulation remains unconvincing and the weakly supervised method proposed  is disconnected to previous weakly supervised methods and it appears hard to connect or re-use in a different setting. Thus, the paper is not suggested for publication in its current form.
Decision: Reject