Kati8/ICLR_2023_2025 huggingface dataset:
authors: Kaizhi Yang
authors: Xiaoshuai Zhang
authors: Zhiao Huang
authors: Xuejin Chen
authors: Hao Su
reviews: 
Comments: In this paper is proposed a NeRF-based approach for simultaneous non-rigid scene reconstruction and rigid part grouping from monocular pictures. To this end, the method combines location-based Eulerian and particle-based Lagrangian interpretations, being a slot attention-based motion grouping network as part of the Lagrangian module. Both interpretations are a hybrid representation of volumetric features, which are supervised by a cycle-consistency loss. Every motion group is expressed by a rigid transformation per temporal frame. Experimental results are provided on synthetic and real datasets, obtaining a good trade-off between accuracy and computational cost in comparison with state-of-the-art techniques.
Strengths: - The paper addresses an important problem in computer vision and learning. The proposed solution is a good combination of multidisciplinary areas. 

- In general terms, the paper is well written and clear enough.
Weaknesses: - The experimental evaluation is provided on simple synthetic datasets where the amount of deformation is a bit limited. Considering the paper formulation, I believe the method could handle strong deformations with no problem, but that is never validated. 

- No full evaluation on real monocular image sequences.
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: 3 good
Clarity: 3 good
reviews: 
Comments: This paper proposes a method for part-aware dynamic NeRF. While dynamic NeRF (D-NeRF) encodes the temporal information as well as the spatial radiance fields, it has not been easy to extract the object part information. To address this problem, this paper uses an idea similar to traditional motion segmentation, which combines the flow between adjacent frames (i.e., Eulerian flow in terms of particle flow simulation) as well as the clusters of motion trajectories (i.e., Lagrangian flow). Experiments show that the proposed method achieves near-SOTA accuracies for novel view synthesis with part-aware representation.
Strengths: + The proposed method correctly brings the idea of using motion trajectories to NeRF-based representations. In particular, the use of a cycle consistency loss that improves the agreement between Eulerian and Lagrangian motion shows a nice coupling of traditional ideas and modern neural networks.

+ The proposed method focuses on scenarios using a monocular moving camera, which makes the proposed method practical. In fact, the paper shows two practical examples of applications to robotics and scene editing.
Weaknesses: - Using long-term motion trajectories is itself a traditional idea in computer vision, especially for motion segmentation (e.g. [a]). While I think that using Eulerian vs. Lagrangian views is a nice introduction to temporal motion representations, it would be nicer if they also briefly discussed the relationship of their idea to these traditional attempts. In my opinion, this would not sacrifice their technical contribution, but rather strengthen the theoretical and academic value of their approach.

[a] Keuper, Margret, Bjoern Andres, and Thomas Brox. "Motion trajectory segmentation via minimum cost multicuts." Proceedings of the IEEE International Conference on Computer Vision. 2015.

- Since their technical contribution may be to combine short- and long-term motion trajectories, readers may want to see the ablation studies comparing with and without these modules.
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: 4 excellent
Clarity: 3 good
reviews: 
Comments: This paper presents an approach for dynamic scene reconstruction that allows the discovery of parts as a result of the factorization of the scene motion. The approach is based on monocular NeRF. A part corresponds to particles that share a common motion pattern. Scene motion is parameterized with both an Eulerian view and a Lagrangian view. The Lagrangian view allows tracking the particles on objects. Since the particles on a rigid part share a common rigid transformation pattern, a motion grouping module is used to detect the parts. The method is based on three modules, namely: the Eulerian module, the Lagrangian module and the canonical module. The Eulerian
module and the Lagrangian module observe the motion of specific spatial locations and specific particles, respectively. The canonical module serves to reconstruct the geometry and appearance for volume rendering. The the reconstruction and part discovery performance  of the method is experimentally evaluated on a synthetic dataset provided by D-NeRF. The metrics used for evaluation were PSNR and SSIM. The motion grouping is evaluated on a synthetic dataset created using Kubric toolkit. In both evaluations the method obtained performances better or at the level of the state-of-the-art.
Strengths: The strength of the paper stems from being a method based on monocular NeRF that besides allowing to perform dynamic scene reconstruction, allows also the discovery of rigid parts. Another strength of the approach is that it used hybrid feature volume and neural network representation, which allows  fast convergence during training.
Weaknesses: The grouping of the parts is based on predicted rigid motion. Rigid motion can be a significant constraint in many applications. The approach is inspired by fluid simulation, with the scene motion being observed from both the Eulerian view and Lagrangian view. The Eulerian and Lagrangian descriptions of a flow field are related by the material derivative. It is unclear whether the method ensures that such relationship is verified or met by the proposed approach.
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good