reviews: 
Comments: The paper presents NeuralPlane, a novel approach to 3D plane reconstruction that utilizes neural fields for generating structured 3D maps from multi-view images without the need for plane annotations. The method emphasizes two main aspects: geometry and semantics. Key contributions include:
1. Monocular Plane Segmentation: A monocular module extracts geometrically smooth (based on off-the-shelves Surface Normal Predictor) and semantically meaningful 2D plane observations (based on Segmenet Anything Model). 
2. Plane-Guided Neural Representation: The model utilizes these 2D segments to train a neural field that captures accurate 3D plane locations. A surface normal regularization and pseudo-depth regularization terms are proposed.
3. Neural Coplanarity Field: This self-supervised feature field enables semantic consistency within the 3D reconstruction by grouping planar regions that share coplanar relationships. A contrastive loss is proposed to distinguish between planes with similar geometric properties but different semantic properties. 
The method demonstrates superior performance on ScanNetv2 and ScanNet++ datasets, indicating its effectiveness in indoor environments.
Strengths: 1. Novelty in Combining Geometry and Semantics: The method’s approach to merging geometry with semantic information through a neural coplanarity field is innovative, enhancing the semantic consistency of 3D reconstructions. A complex system with multiple stages is proposed to estimate/reason the local planar regions and the associated parameters, to associate the planes in 3D using radiance field, to resolve semantic conflicts using Neural Coplanarity Field.
2. High-Quality Reconstruction: Experimental results indicate that NeuralPlane achieves fine-grained and coherent plane reconstructions, outperforming existing methods in most of the metrics. 
3. Efficiency: NeuralPlane’s volume density representation allows for faster training compared to implicit methods, an important practical advantage.
4. Extensive ablation studies: the authors include sufficient ablation studies to support the effectiveness of the proposed modules.
5. Good writing: the paper is clearly written that the technical details are clearly presented.
Weaknesses: 1. Complexity of Methodology: The proposed method involves several stages, including monocular plane segmentation, neural coplanarity field training, and plane extraction. In each stage, there are several submodules and I believe there are some hyperparameters decisions in each stage. While effective, this complexity (especially the combination of submodules and associated parameters) may impact its scalability to larger scenes or generalizability. For example, K-means clustering on predicted normal map, mask size threshold in SAM, thresholds to form negative pairs in Neural Coplanarity Field, Loss balancing parameters, etc. It would be great if the authors could share the insights on the impact of these hyperparameter settings on different scenes. For example, is a universal set of hyperparameters applied to all the test scenes? It would be great if the authors could provide a sensitivity analysis or ablation study on key hyperparameters across different scenes. This would help clarify how robust the method is to parameter changes and whether a universal set of parameters is feasible.

2. Dependence on Initial 2D Plane Segmentation Quality: As the regularizations are based on the quality of initial local plane geoemtry, the method’s success can depend on the quality of 2D plane segments obtained from monocular priors, which may introduce inaccuracies in challenging environments for monocular predictors. As the authors mentioned , the local planar primitives can result in severe inconsistency across views (Line 194). 

3. Over-Segmentation Issue: As highlighted in the paper, the Segment Anything Model (SAM) tends to over-segment planes, resulting in multiple smaller plane segments for a single surface. Although this is managed in the training process, it may require further refinement to avoid segmentation inconsistencies in complex scenes. From the visualization on the GitHub page, it seems to me that some of the small segments usually resulted in inaccuracy.
Recommendation: 10
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 4