Kati8/ICLR_2023_2025 huggingface dataset:
authors: Hanqiao Ye
authors: Yuzhou Liu
authors: Yangdong Liu
authors: Shuhan Shen
reviews: 
Comments: This paper proposed a framework called NeuralPlane to reconstruct 3D indoor scenes as planar primitives from posed 2D images. The author first employs 2D prior models to generate local planar primitives, then uses the geometric and semantic priors to guide the NeRF-style reconstruction learning. Finally, a decoding algorithm is designed to extract the global explicit plane mesh from the learned neural field.
Extensive experiments are conducted to evaluate the performance on ScanNetv2 and ScanNet++ datasets.
Strengths: This paper proposed a comprehensive 3D reconstruction framework with plane primitives. 

* Compared to some of the similar works that only focus on detecting the planes, this work also utilizes the detected planes to guide neural field learning.

* The presentation of this paper is clear and concise. 

* The qualitative and quantitative results look promising.
Weaknesses: * This work involves a lot of submodules, especially the neural field with geometric, semantic, and coplanar features could be computationally expensive.

* A concurrent work is very relevant to this paper and could be discussed in the related works section:
Chen, Zheng, et al. "PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields." arXiv preprint arXiv:2401.00871 (2023).

* The text illustration and conceptual figure of Neural Parser could be improved, current version is not clear enough and easy to follow.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper presents NeuralPlane, a method for reconstructing 3D scene plane primitives via neural fields without GT plane labelling. The method is divided into three main stages: firstly, it combines pre-trained normal prediction and SAM to generate initial 2D planar segments and estimates their 3D parameters using SfM keypoints. Secondly, it optimises two neural fields, a density field based on planar geometric constraints, and a coplanar neural field that understands the semantic relationships between regions . The neural coplanar field is followed by a neural parser module that helps to model the learned coplanar relations. Finally, the optimised neural representations are converted into explicit 3D planes through point sampling, feature-based clustering and RANSAC fitting. The method is evaluated on the ScanNetv2 and ScanNet++ datasets, and the results show that it outperforms both the learning-based method and the Geometry+RANSAC method in terms of geometric and semantic metrics.
Strengths: - This paper is well-written and easy to understand.
- The proposed method does not require ground-truth plane annotations, as it can learn effectively from noisy monocular model outputs.
- The method demonstrates SOTA performance and achieves clean plane segmentation results.
Weaknesses: - The proposed method involves numerous hyperparameters, including balancing parameters for loss, the number of semantic prototypes, and parameters listed in Lines 850-863.
- As a complex system, it is important to discuss and present failure cases to help readers understand the method’s limitations.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3