From: ICLR.cc/2022/Workshop/OSC/Program_Chairs
title: Paper Decision
decision: Accept (Poster)

From: ICLR.cc/2022/Workshop/OSC/Paper7/Reviewer_6vo5
title: Interesting combination of Slot Attention and NeRF with solid experiments
review: The paper tackles the problem of inferring 3D object-centric representations from single views without supervision. The object representation uses Neural Radiance Field (NeRF), with additional inputs specifying the object shape and appearance. These additional inputs are inferred by a Slot Attention encoder. Given the inferred representations, the decoder can compositionally render the objects. The experiments show that the proposed model (1) achieves better reconstruction than a non-object-centric NeRF-AE baseline on CLEVR with 2-5 objects, (2) supports scene manipulation by adding/removing/swapping objects, (3) achieves similar 2D segmentation performance as Slot Attention, IODINE, and MONet on CLEVR6, and (4) the learned representations are helpful to the CATER snitch localization task.

Pros
- Inferring 3D object-centric representations from single views is a very challenging problem
- The paper evaluates the learned representations through a comprehensive set of experiments
- The model is able to perform novel view synthesis under small camera pose modifications even though it is not trained with multiple views

Cons
- The paper can be further strengthened by including more quantitative evaluation for the 3D aspect of the learned representations
rating: 3: Strong accept
confidence: 3: The reviewer is confident that the evaluation is correct

From: ICLR.cc/2022/Workshop/OSC/Paper7/Reviewer_WvjL
title: Accept
review: This paper is relevant to the topic of the workshop.

This work appears very similar to SPACE (https://arxiv.org/abs/2001.02407). It is not clear to me how this is a 3D representation? The methods section is very brief. 
Authors show improvement over Nerf with a single object in an auto-encoder setup.
Results in Figure 2 look good, however, some objects disappear under the transforms. It would also be help to highlight which object has been added.
rating: 2: Good workshop paper, accept
confidence: 2: The reviewer is somewhat confident that the evaluation is correct