reviews: 
Comments: This paper proposes a novel method for lifting a few unposed images directly into a 3D representation as well as estimating the corresponding camera poses. The key contributions are a novel object-centric pose estimation pipeline based on the Perspective-n-Point (PnP) algorithm, scaling up training to achieve strong cross-dataset generalization, and instant estimation of the underlying NeRF (as opposed to training offline via gradient descent). The authors demonstrate impressive results on object-centric scenes across datasets.
Strengths: -The motivation for the method is clear - reconstructing object-centric 3D scenes without knowledge of the camera poses.
-The method is simple (which is a good thing) and straightforwardly scalable.
-The approach for camera pose estimation is neat, containing an attractive 3D inductive bias and mostly outperforms common object-centric pose estimation methods.
-Generated results are strong and aesthetically pleasing, with significant cross-dataset generalization demonstrated.
-Ablations are informative.
Weaknesses: -I have a suspicion that the baselines - RelPose++ and Forge - are not trained on the same datasets, but that the authors are rather using the pre-trained methods and just evaluate them on these datasets. Could the authors clarify?
- One baseline is very clearly missing - the Scene Representation Transformer, Sajjadi et al, which can similarly perform novel view synthesis from unposed images at test time while requiring camera poses at training time. 
- The prior point is even more critical since the authors don't always outperform RelPose++ on Co3D. If RelPose++ wasn't trained at a similar scale, this is concerning.
- In the evaluation section, the authors state that they evaluate PSNR against the input images. That is questionable - they should evaluate the PSNR against held-out test views?
- The authors should clarify - already in the introduction and the abstract - that their method still requires ground-truth poses at training time. I.e., it is only pose-free at test time!
- Why do the authors choose to report results on Co3D in the appendix as opposed to the main paper? I don't see a principled difference here. Just as the other results, this result should be reported in the main paper. The fact that their method requires training on an additional real-world dataset is important and relevant and should not be hidden in the appendix.
- The authors should demonstrate their method on some real-world captures instead on only synthetic examples.

Relevant references missed:
- Triplanes for neural fields were first proposed in "Convolutional Occupancy Networks", Peng et al. and should be discussed.
- The paper "FlowCam" also incorporates 3D shape information during the camera pose prediction process and should be discussed.

Minor concerns:
- In all the tables, the best-performing method should be bolded.
- "output-performing" --> outperforming (last paragraph before related work)
- I would recommend to avoid phrases like “pretty well” - sounds a bit casual.
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 3 good