Venue: CoRL 2021 Conference

Reviews:

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper applies the wildly popular and promising NeRF -- wherein we learn a radiance field network that is integrated along a ray, resulting in estimates of the RGB and depth -- to the setting of transparent object detection for robotic grasping. For the latter, the paper makes use of another existing and widely-used method, DexNet. 

The main contribution of the paper is showing that depth maps produced by NeRF integration are of sufficient high-quality for robotic grasping. This is important because typical 3D perception sensors relying on infrared imaging do a very poor job at handling the specularities and transparent nature of glass, the depth maps they output are almost unusable for manipulation. A secondary contribution is the creation of datasets of transparent objects, both synthetic and real.         
strengths_and_weaknesses: Strengths

S1. The paper is well written and it addresses a very important and challenging problem in a practical way.

S2. It confirms that the NeRF depth map is practical for depth-based grasping and manipulation, which is very important.

S3. It contributes to addressing the issue of data collection for pose detection and grasping of transparent objects, without a need for object models and without a lot of need for manual handling of the objects in the scene. I think this is also very important.

Weaknesses

W1. Technically speaking, the proposed method is not novel. It "just" combines two existing methods. In fact, the original NeRF paper showcased a few examples of how it worked with transparent objects. That said, I think the whole is more than the sum of its parts here, and the paper offers many insights and experimental results about the performance of NeRF in a wide variety of transparent objects. I think this worthwhile enough for me to completely ignore the issue of novelty.

W2. I'd have appreciated some more details about the size of the 3 contributed datasets and how they differ from other papers proposing similar datasets. 

W3. I'd have appreciated some discussion on the relationship between NeRF and ray-tracing methods. Can NeRF handle second-order light effects from ray bounces and shadows? What happens when you have transparent objects occluding other transparent objects?

W4. Section 4.2 was quite unclear to me. After reading it, it was still ambiguous whether the proposed method uses view-independent or view-dependent volume density sigma?

W5. It's not very clear how or whether the ground truth object pose was collected in the Cannon EOS datasets.  

W6. The future work directions are mostly about speeding up the training NeRF, and not necessarily about increasing its accuracy. I'd have appreciated some discussion in that direction. 
summary_of_recommendation: I am recommending that the paper be accepted. It addresses a very important and challenging problem in a way that alleviates the need for calibrated data collection, which is a very tedious process. While I would have liked some more extensive discussion on W3, W4, and W6, I find the paper to be useful enough to push this sub-area of robot perception forward.  
issues: It would be great if weaknesses W2-W6 could be addressed by the rebuttal deadline. They'd place the paper in much better context.
review_title: null

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: The paper presented a method on recovering depth of transparent objects with "Neural Radiance Field", and the potential application in grasping. While the recovering depth part showed meaningful results, the paper does not provide much data point on the application of grasping, thus the main focus is on depth recovering.
strengths_and_weaknesses: Strengths: adopted NRF to recover depth of transparent objects, which is usually hard for conventional approaches.

Weaknesses: Does mention grasping in the title but not providing sufficient amount of data in experiments.
summary_of_recommendation: Unless provide more data on grasping or switch to focus purely on depth recovering, I would suggest on weak reject.
issues: 1 grasping should include more details, e.g. examples, experiments, metrics, so that reader could see the difference between having the proposed approach and baseline.
2 more details on experimental setup, e.g. how the cameras are configured, what metrics has been used. more quantitive results on a set of defined metrics.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper tackles the problem of grasping transparent objects by using combination of NeRF and dexnet. It takes in multiple view of the scene and trains a nerf model that underneath predicts occupancy probability of each point in the workspace volume. They also add some small modifications to nerf where it reasons about view independent occupancy and also instead of aggregating the occupancy probabilities along the ray it thresholds the occupancies and terminates the depths at the first location that has a probability higher than a threshold. From the predicted depth, they use dexnet to generate grasps and pick up the objects.

strengths_and_weaknesses: Strengths:
- This is a systems paper on how to integrate different modules properly to solve the task. I like the idea of using nerf for depth prediction and using dexnet
- The idea of using more lights to improve the depth quality also makes sense.

Weaknesses:
- What is the success rate on the whole system in grasping real transparent objects?
- How long does the training of nerf takes?
- What are the failure cases look for depth prediction and (depth prediction and grasp generation) look like?
- As far as I understand NeRF, \sigma is a probability between 0 and 1 for likelihood of a point being occupied or not. However, the thresholds are all more than 1. I would like to get clarification on why all these numbers are greater than 1.
- video of real robot experiments missing.

summary_of_recommendation: I like the general idea of the paper but I think the paper focuses too much on just depth prediction and I'd like to see how authors responds to the points I raised above.
issues: See weaknesses above.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper proposes to render depth maps of transparent objects with NeRF. The rendered depth maps can be used for grasping these transparent objects.
strengths_and_weaknesses: Strengths:
- NeRF can model transparent objects accurately. Based on the learned NeRF representation, depth maps can be rendered.
- This paper shows that using a threshold on density to locate boundary of the object and placing additional lights in the scene can improve the quality of the learned geometry.

Weakness:
- No quantitative comparisons. For example, the quantitative comparison between the depth maps rendered by vanilla NeRF and the proposed method (in simulation), and the grasp success rate given depth maps from RealSense, vanilla NeRF, and the proposed method (in simulation and real-world).
- There's not much novelty in the method part. It's just utilizing an existing method (NeRF) to a relatively new problem (render depth map of transparent objects).
summary_of_recommendation: It's an interesting idea to render depth maps of transparent objects with NeRF. However, the method is only slightly different from NeRF in depth rendering part. Most importantly, no quantitative experiments are conducted to prove the validity of the method. Only qualitative results are shown. It's hard to judge the quality of the obtained results.

Given the authors' concrete response, I would like to change to weak accept.
issues: As stated in weakness. 

Also, the proposed method only uses reflection to locate the surface of the object. One important property of transparent objects, the distortion is not handled. If the method can utilize the clues of distortion, the few light sources would be enough to mode the geometry. That would be very interesting.
review_title: null

Meta-review: The paper's contributions in regards to depth estimation have been well received, both in terms of its methodology and on the contribution of datasets. The writing is clear and the reporting of the investigations is broad and insightful. The methodology is sensible and original while its motivation is easy to understand.  

However, it seems there is confusion when it comes to robotics evaluation. One interpretation is that there was no real grasping experiment at all, although Figure 1 implies that Dex-Net was used for the Fetch robot (if Fig. 1 is a simple illustrative figure this should be made clear). Assuming that no real-robotics experiments were done then sentences such as "Experiments on synthetic and real images showing NeRF with Dex-Net generates high-quality grasps." and "Grasping real-world Objects" are not supported with experimental, quantitative evidence (e.g. success rates). On the other hand, if real grasping experiments were done, then the paper is missing deeper evaluations which should be added or at least clarified during the rebuttal.

Post-rebuttal ====================

The authors clarified the main concerns and weaknesses during the meta-review. As a result, the reviewers have been positive in raising their scores. The authors now provide strong experimental evidence on the efficacy of their method.
Decision: Accept (Poster)