reviews: 
Comments: This paper presents a neural 3D reconstruction system on 3D plane reconstruction of indoor scenes. Inspired by the recent success of neural radiance field and image foundation models (SAM2), this paper presents a multi-view 3D reconstruction pipeline leveraging these techniques. The training scheme consists of three phases: Initializing plane segments and parameters -> optimizing a neural feature field for plane-specific feature representation (encouraged by a list of geometry-guided loses) -> plane extraction by grouped features and RANSAC. Experiments are conducted extensively on two representative indoor datasets: ScanNet and 7-scenes and are compared with a group of competitve baseline methods.
Strengths: 1. The unique advantage of the system is the association of geometry and semantic features for plane reconstruction problem, which requires both geometry-aware perception and semantic-aware grouping.
2. The paper leverages foundation models to achieve plane segment initializaing and utilize geometry-driven losses to optimize the system. There is no groundtruth plane segmentation or geometry label required. 
3. Overall the paper and diagrams are well written and presented.
4. The experimental comparison and thorough and convincing on both plane geometry reconstruction and segmentation.
Weaknesses: 1. I think the major advantage of this paper is the unsupervised learning paradigm on the plane reconstruction problem. However, since both ScanNet and ScanNet++ has groundtruth images, this unique advantages seems not to be fully enjoyed and reflected. So I suggest authors to apply the proposed system on some outdoor scenes containing plane structures (such as autonomous driving dataset or street view datasets), to verify the adaptability of the method.
 
2. I have tested PlaneRecon in my previous projects. It can incrementally reconstructs planes in an online and real-time manner. Besides, it can trains over multiple scenes and directly test without any test-time optimization. Although its precision should fall behind than the neural reconstruction papers, the generalizability and speed is higher than the proposed method. I am curious on whether the paper has such potential to tackle these limitations.

3. Missing a few related works: (1) Recovering 3d planes from a single image via convolutional neural networks (2) PlaneMVS: 3D Plane Reconstruction From Multi-View Stereo (3) Single-image piece-wise planar 3d reconstruction via associative embedding.

4. On quantitative evaluation, it is unclear that what is the groundtruth is here. Does it stand for the groundtruth planar part only or the entire mesh? As a plane reconstruction paper, I think the former one should be more reasonable. If so, the first several methods listed in Table 1 cannot directly be compared with the proposed method. Authors should make it clearer on this part.

5. Is the proposed method robust to the hyperparameters listed in the paper especially (1) to,tn and m in the push loss during training and (2) the parameters selected in RANSAC during plane fitting? Some ablation studies on hyper-parameter robustness are expected to make the method more generalizable across most indoor scenes, since the geometric scale and semantic distribution can have large variance among scenes.
Recommendation: 8
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 3