reviews: 
Comments: This paper describes LEAP, a method for novel view synthesis from 5 or fewer images. Unlike other novel view synthesis methods, LEAP does not require camera poses to be provided at test time. However, LEAP still requires camera poses at training time.
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LEAP generates novel views using a sequence of image- and voxel-based attention layers. Given a set of input views, it arbitrarily designates one view as the canonical view. It uses this view to define a world coordinate space in which novel views can be queried. After having picked the canonical view, LEAP encodes the context views, alternating between self-attention among all views and cross-attention between queries from the non-canonical views and keys/values from the canonical view. It then lifts the resulting image features to 3D via cross-attention between learned (shared across scenes) query tokens in a 3D voxel grid and key/value tokens from the images. Finally, it maps the voxel grid's features to a density and renderable feature at each location, uses NeRF-style volumetric rendering to composite these features, and finally converts the rendered features to an RGB image using a 2D convolutional network.
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LEAP is trained using a mean-squared error loss on RGB values, an image-based perceptual loss, and a mask loss (if masks are available). It is important to note that although the rendering process itself does not require camera poses, the training process does, since the views that are rendered to compute these losses are queried using known poses.
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Strengths: ​* LEAP is among the first methods that perform novel view synthesis without known poses at test time. While SRT also performs novel view synthesis without known poses at test time, LEAP has better results on object-level scenes and guarantees consistency between viewpoints by using an explicit 3D representation.
* The authors provide video results that convincingly show LEAP working qualitatively on a large number of objects.
* LEAP outperforms the provided baselines on object-level scenes.
* The description of the method is clear and easy to follow. Additionally, the authors provide concise code for the LEAP model that is helpful for understanding the details.
Weaknesses: * The authors claim that they significantly outperform prior methods on scene-scale datasets. However, LEAP only presents a comparison on the small DTU dataset. This comparison is somewhat flawed, since it prevents the authors from benchmarking against SRT, which is focused on scene-scale novel view synthesis. It would be much more convincing to see results on MultiShapeNet, which is a publicly available scene-scale dataset that SRT is trained and compared on. Showing more scene-scale results seems particularly important because LEAP's use of a 3D voxel grid with fixed extents seems likely to limit its usefulness on scene-scale datasets. It is also worth noting that the scenes in the DTU dataset have small camera baselines, meaning that classical pose estimation would produce near-perfect estimated poses on DTU. Using a wide-baseline dataset for scene-scale comparison (where pose estimation is more likely to fail) would be more convincing.
* Figure 8 appears to be flawed and somewhat misleading. The experiment setup, as I understand it, is that the authors feed a synthetic scene consisting of a single dot projected onto five images into a trained LEAP network. The authors show that the neural volume's density is highest along the ray that corresponds to the dot's location in the canonical view. This is good evidence that the network has "understood" the meaning of the canonical view and has learned to map pixels to rays within the volume. However, the authors claim that the network then "leverages the multi-view information to resolve the depth ambiguity of the ray." However, estimating pose given images of a single dot is impossible, since the scene's appearance is invariant to rotation around the dot. Since pose and correspondence are both needed to correctly place a 3D point, this means that in this case, the network cannot actually be doing what the authors claim. It seems much more likely that because the training dataset consisted of objects/surfaces that were all at roughly the same depth, the network is simply placing the dot near the mean of those depths.
* The authors should explicitly clarify both in the intro and the methods section that their method requires camera poses at training time. Currently, the first sentence of section 3 might suggest that their method doesn't require poses at training time either.
* The authors cannot both claim that their method is the first to introduce the pose-free paradigm (first paragraph, page 4) while also stating that the SRT already introduced the same paradigm (which it very much did). The authors should soften that claim to saying that they merely implement a new way of solving this pose-free problem, rather than introducing it.


### Minor Points
* The authors state that the 2D-to-3D step happens in a coarse-to-fine manner, but do not describe how. It seems that the network emergently shows coarse-to-fine behavior (boundaries getting sharper during successive lifting attention layers), not that there's an upscaling operation between lifting attention layers.
* The paper contains a large number of typos and grammatical errors that need to be fixed (too many to list).
* In figure 2, the colors for fusion with high weight and fusion with low weight are extremely similar when printed.
* It would be worth mentioning "RUST: Really Unposed SRT," "View Matching Neural Radiance Fields," and "GAN-Based Neural Radiance Field Without Posed Camera" in the related works section.
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good