KaRF: Weakly-Supervised Kolmogorov-Arnold Networks-based Radiance Fields for Local Color Editing
Authors: Wudi Chen
Zhiyuan Zha
Shigang Wang
Bihan Wen
Xin Yuan
Jiantao Zhou
zipei fan
Gang Yan
Ce Zhu
Venue: NeurIPS 2025 poster ()
Keywords: Neural Radiance Fields
Local Color Editing
Kolmogorov-Arnold Networks
Primary area: deep_learning

Abstract: Recent advancements have suggested that neural radiance fields (NeRFs) show great potential in color editing within the 3D domain. However, most existing NeRF-based editing methods continue to face significant challenges in local region editing, which usually lead to imprecise local object boundaries, difficulties in maintaining multi-view consistency, and over-reliance on annotated data. To address these limitations, in this paper, we propose a novel weakly-supervised method called KaRF for local color editing, which facilitates high-fidelity and realistic appearance edits in arbitrary regions of 3D scenes. At the core of the proposed KaRF approach is a unified two-stage Kolmogorov-Arnold Networks (KANs)-based radiance fields framework, comprising a segmentation stage followed by a local recoloring stage. This architecture seamlessly integrates geometric priors from NeRF to achieve weakly-supervised learning, leading to superior performance. More specifically, we propose a residual adaptive gating KAN structure, which integrates KAN with residual connections, adaptive parameters, and gating mechanisms to effectively enhance segmentation accuracy and refine specific editing effects. Additionally, we propose a palette-adaptive reconstruction loss, which can enhance the accuracy of additive mixing results. Extensive experiments demonstrate that the proposed KaRF algorithm significantly outperforms many state-of-the-art methods both qualitatively and quantitatively. Our code and more results are available at: https://github.com/PaiDii/KARF.git.

KaRF: Weakly-Supervised Kolmogorov-Arnold Networks-based Radiance Fields for Local Color Editing
Wudi Chen Jilin University
Zhiyuan Zha Jilin University
Shigang Wang Jilin University
Bihan Wen Nanyang Technological University
Xin Yuan Westlake University
Jiantao Zhou University of Macau
Zipei Fan Jilin University
Gang Yan Jilin University
Ce Zhu
University of Electronic Science and Technology of China
KaRF
Segmentation Stage
Lccl Recoloring Stage
Palette-basee based Local Color Editing
Multi-view Input Many-to-one Editing
One-to- Editing
Figure 1: We propose KaRF, novel weakly-supervised method for local color editing of neural radiance fields. KaRF constructs two-stage Kolmogorov- Retworks-based Radiance Fields, which achieves precise region segmentation and efficient local recoloring tasks. Our method enables accurate and natural local color editing of arbitrary regions within 3D scenes, while also supporting one-to-many or many-to-one editing mappings, greatly enhancing the flexibility and expressiveness of local color editing.
Abstract
Recent advancements have suggested that neural radiance fields (NeRFs) show great potential in color editing within the 3D domain. However, most existing NeRF-based editing methods continue to face significant challenges in local gion editing, which usually lead to imprecise local object boundaries, difficulties maintaining multi-view consistency, and over-reliance on annotated data. To address these limitations, in this paper, we propose a novel weakly- weakly-supervised method called KaRF for local color editing, which facilitates high-fidelity and realistic appearance edits in arbitrary regions of 3D scenes. At the core of the Networks
proposed KaRF approach is unified two-stage Kolmogorov-Armn (KANs)-basee radiance fields framework. comprising segmentation stage fol- lowed by a local recoloring stage. This architecture seamlessly integrates geometric priors from NeRF to achieve weakly-supervised learning, leading to superior per- formance. More specifically, we propose residual adaptive gating KAN structure, which integrates KANN with residual connections, adaptive parameters, and gat- ing mechanisms to effectively enhance segmentation accuracy and refine specific
*Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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editing effects, Additionally, we propose a palette-adaptic reconstruction loss, which can enhance the accuracy of additive mixing results. Extensive experiments demonstrate that the proposed KaRF algorithm significantly outperforms many state-of-the-an methods both qualitatively and quantitatively. Our code and more results are available at: https://ithub..cm/ watpp////.ocomaaai/////A......
1
Introduction
Neural radiance fields (NeRFs) 29 are capable of constructing high-quality 3D scenes and rendering fine-grain fine-grained and photorealistic novel views by fusing 2D
images with camera pose 3D scene modeling capability, NeRF has been
information. With its implicit representation-drvve successfully applied in various domains [16 13 and scenes [31 2 38 Nevertheless, the effective utilization of the 3D scene neural representation learned by NeRF to achieve precise local editing while maintaining a high degree of realism remains a critical frontier for further exploration. This challenge arises from the fully connected architecture used by NeRF, where adjusting a single parameter triggers global changes across all parameters, making the precise extraction of local regions in scene space extremely complex. Mcanwhile, under the constraints of vrews consistency, performing detailed and natural edits is also subject to significant limitations.
To address the challenges of NeRF local color editing, one class of methods focuses on image segmentation models, achieving local editing by distilling 2D semantic features into 3D feature fields [22 20). However, this category of methods often leads to color bleeding in non-edited regions. Other methods acquire local regions through point cloud projection 23 or voxel expansion followed by point cloud extraction 32 17 Due to the discrete nature of point clouds and the fixed volume of voxels, these methods have limited ability to accurately delineate the contours of local objects. Additionally, these methods struggle to distinguish between the approximate colors of objects, which limits their potential applications in the field of local editing.
Bearing the above concerns in mind, in this paper, we propose KaRF, a novel -eakly-supervised method for local color editing by leveraging the efficient approximation ability and outstanding nonlinear expressive power of Kolmogorov-Amon Arnold Networks (KANs) [26]. To the best of our knowledge, this is the first work to apply KAN to NeRF-basee realistic editing for arbitrary regions. Using only one or three coarse, automatically generated masks per scene with no manual refinement, the proposed KaRF approach achieves consistent and precise local color editing effects, as shown in Figure []] Specifically, we model local color editing as unified two-stage process comprising segmentation and local recoloring. To this end, we propose a residual adaptive gating KAN structure that accurately segments arbitrary regions and performs localized optimization in accordance with user-specified color requirements. By incorporating geometric neural representations derived from the pre-trained NeRF, our proposed KaRF approach effectively addresses inconsistencies, low quality, and under-samplie masks additive mixing weights from multi-view inputs. To further enhance model ficxibility and training stability, we introduce multidimensional adaptive parameters. Based on this structure, we build radiance field models tailored each stage and design palette- adaptive reconstruction loss during the local recoloring stage to ensure a coherent distribution of the base colors in 3D space. Finally, KaRF forms an integrated, high-quality editing approach that seamlessly combines segmentation and local recoloring. The significant contributions of this paper are summarized as follows:
(1) We propose the KaRF framework for local color editing, which enables users to selectively recolor arbitrary regions while requiring minimal guiding information.
(2) We propose a novel residual adaptive gating KAN structure and palette-adaptive recon- struction loss to achieve precise segmentation and local recoloring.
(3) Extensive experiments demonstrate that the proposed KaRF algorithm significantly outper- forms many state-of-the-a methods both qualitatively and quantitatively.
2 Related Work
2.1 Segmentation in NeRF
NeRF segmentation tasks aim to generate high-precision and multi-view consistent segmentation masks, which are usually classified into two categories: ciiturr-lligmmeet-bbaeed methods [12 [8 20

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and 2D model-based methods [30 77. Seature-alignmee brligmmmeet--eeeddddd based methods constructed view- consistent segmentation clusters by aligning 2D visual features with additional features fields. For instance, Semantic-NeRF 39 extended sparse semantic labels into dense semantic annotations by jointly encoding geometric, appearance, and semantic information. NeRF-SOS [2] integrated appearance features and geometric information into the segmentation field to produce segmentation masks. Conversely, 2D model-based approaches leveraged existing image segmentation priors to guide and constrain the generation of 3D segmentation masks. For example, the MVSeg module in SPIn-NeRF 30 and SA3D 7 generated multi-view consistent segmentation masks based on foundational 2D segmentation models 66 19]. However, SPIn-NeRF suffered from noisy outputs due to insufficient integration of 3D information, whereas SA3D tended to generate segmentation masks with blurred boundaries under conditions of significant viewpoint variation. Compared to the aforementioned 2D model-based approaches, our approach exhibits superior performance terms of both segmentation accuracy and consistency.
2.2 Color Editing in NeRF
Color editing tasks aim to modify the visual attributes of objects (e.g., color, hue, and brightness) while maintaining photorealistic fidelity. Previous works [33 3 4 combined NeRF with physics- based rendering to explicitly decompose properties such as reflectance, geometry, and lighting. enabling realistic scene reconstruction and flexible control under varying lighting conditions. On the other hand, Upst-NeRIF [10 and SttcNeRF focused on optimizing NeRF, concentrating mainly on preserving geometric details and texture consistency of the scene under stylization constraints. However, recent advanced approaches, such as PaletteNeRF [22 35] and RecolorNeRF [44] applied palette-based color editing to NeRF to achieve multi-view consistent and realistic effects. By optimizing palettes and decomposing color layers, enabling intuitive recoloring and high-fidelity editing of complex scenes. To further extend local editing capabilities, ICENeRF (23] enabled local recoloring in NeRFs by decoupling the perceptual optimization of color MLP weights. However, this method can struggle with precise local color control, and its foreground mask requirement scales with the number of colors being edited. IReNe (27] improves local editing speed by fine-tuning the last layer of its color MLP learn local colors. Nevertheless, it can suffer from color bleeding in occluded situations and requires precise manual maps and new reference images for multiple edits. LAENeRF (32), through color layers decomposition based on ray termination points and voxel-based region expansion, achieved realistic editing of local regions. However, voxel expansion may result in imprecise region selection, which may affect subsequent appearance editing. In contrast, KaRF demonstrates superior localization and multi-view consistency in color editing.
3 Preliminary
KAN. Multilayer perceptrons (MLPs) 15 have become the cornerstone of numerous modern neural networks. Recently, Colmogorov-Amole Networks (KANs) (26 have been introduced as an alternative to MLPs. They are based on the Kolmogoroy-Amold representation theorem [1]. which asserts that continuous function. Consequently, both MI.Ps and KANs can be regarded as models that utilize functional compositions and combinations express complex mappings. In MLPs, however, the functions are represented by fixed activation functions applied to nodes, while the combinations are achieved through linear weight matrices connecting neurons. In contrast, the functions in KANs are learnable univariate functions on edges, while the nodes perform simple addition operations. The overall structure of an N-layer KAN defined
of compositions of several univariate functions represent any multivariate
KAN(X)= =(P 2N c OP))X
where T denotes the 1-th layer within the KAN model and is composed of 1D learnable activation functions (Φ):
T = =(
i i= 1,2, Ran:
j == Nout,
where Vin and Poot are the number of input and output nodes a single KAN layer, respectively. The computation from layer to layer I + I in the KAN model can be represented in the following
3

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Mulli-view Images
KaRF
Color Editing
Cave
Pretrained NeRF
Segmentation Stage Point-Prompteed SAM
Local Recoloring Stage
Paletto-Bused Layer Decomposition
Preprocessed
Spatial-lovel
User Interaction
Masks
Convex &ellettes&&Wighhhn
Figure Overview KaRF. We employ a pre-trained NeRF (29 to extract the output of its density MLP (i.e., 3D point density and multi-channel features Sprior E _2256 containing scene prior knowledge). Subsequently, NeRF parameters are frozen, and a two-stage training process is initiated: 1) Segmentation stage. Utilizing multi-view images and an initial set of 1-3 coarse segmentation masks generated by point-prompted SAM [T9], we generate consistent masks by constructing KAN-based segmentation radiance fields, which is supervised by Lseg. These refined masks are then passed to the next stage. 2) Local recoloring stage. We adopt the strategy from LoCoPalette g to compute convex hulls for the masks and perform layer decomposition, thereby obtaining initial palettes and weights. These parameters are then fed into a KAN-based recoloring radiance fields, aimed the tine-grained reconstruction of weights in 3D space and the adaptive optimization of the palettes. This stage is supervised by L and Lraleue. The trained palettes and weights enable users interactively composite the scene by directly modifying colors in the palettes.
matrix form:
(1..1())
)12(()
I111 (-)
(122(())
222(())
(1)
X
X
(3)
@lmemna)))
2())
Φ semen (+)
where X; Rot represents the input to layer and X R is the resulting output of the same layer. Φ(.) denotes a learnable univariate function, parameterized as a B-spline curve [11] with learnable coefficients. Each (-),,(()) models the relationship between the j-th output node and the i-th input node in layer I, enabling flexible, non-linear mappings between neurons. Mathematically, ((-) can be expressed as:
+(-) = wxb(-) (-6(()++))) + w.E((),
where 6(.) = silu(.) and E(-) s.)ine(.) = Σ. c₁ By (.)... For more details on KAN, please refer to (26.. Due to the superior representation performance for complex nonlinear mappings, KAANs can describe complex mappings with fewer parameters, demonstrating superior approximation capabilities and offering greater flexibility and efficiency compared MLPs. Furthermore, the use of simple functional combinations makes the internal mechanisms of KANs more interpretable.
Methodology
In this section, we describe the details of KaRF (Sec. 4.1. followed by an introduction to the
two-stage process of local color editing (Sec. [4.2 and the optimization strategies for each stage (Sec. Figure 22 illustraaes overview of our two-stage pipeline.
4.3
4.1
KaRF
In NeRF (29], the inductive bias introduced through staged network design effectively constrains the solution space for complex function fitting, providing accurate geometric structure appearance priors for NeRe-based downstream tasks (e.g. segmentation, color editing), thereby enabling higher- quality outputs in post-pros post-processing. Building on this, KANs (26), with their exceptional nonlinear expressive power, particularly well-suited for fitting complex mappings. Leveraging this strength

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Linear
KAN
Sigmoid
f prior
P
KAN
Sigmoid
S
Ceee
SILU
KAN
Volume Rendering
W
wiight
((d)
fh
W.P W.P
dropout
Concatenate Multiply @ Add [ Learnable parameters
Only segmentation stage [ Only local recoloring stage
(())
Figure 3: Our network structure. Our KAN-based segmentation radiance fields utilize positional encodings y(() and prior features fprior to output per-point probability distribution & e [0, 1)% over N& predefined classes. Subsequently,
KAN-based recoloring radiance fields then incorporate the viewing direction 7dd) in addition to the aforementioned inputs, predicting a per-point weight vector w E [0, 2 corresponding to palette P containing N, colors. Both s and w are volume rendered to yield per-pixel segmentation map S and weight maps W.
and the priors from NeRF, we have constructed a versatile model centered around KAN, with its major components as follows:
GRBFKAN layers. B-spline functions, owing to their piecewise polynomial nature, exhibit inherent advantages in modeling smooth functions. However, in scenarios involving high- high-frequency spatial details (e.g., specular highlights abrupt color transitions), such as local color editing, the representational accuracy of B-splines can be constrained. In view of this, we replace B-spline functions in vanilla KAN (26] with Gaussian Radial Basis Functions (GRBFs) [5] GRBFs, utilizing Gaussian kernels, possess a key advantage in the adaptability of their scale parameters, enabling them to more effectively approximate color details characterized by drastic local changes or sharp features. Specifically, each KAN in KARF composed GRBFKAN layers, in which the response value of each GRBF within these layers depends the Euclidean distance between the input and its respective center point e₁. This mechanism not only endows the model with excellent non-linear approximation capabilities and greater flexibility but also, in some cases, leads to improvements in computational performance [25]. Within this framework, {xx) is represented as a GRBF function with N centers as follows:
||
c2 2h2
[(x) Σ
Wi exp
(5)
where w, denotes the trainable weight, and represents the bandwidth of the Gaussian kernel, which modulates the dispersion of the function and controls its response range.
Residual Adaptive Gating KAN. Although stacking multiple layers of KAN can enhance the non- linear expressive capacity the network, it leads to training instability and increases computational overhead. Consequently, we propose KAN-based residual adaptive gating structure, illustrated in Figure 3 This structure, serving as a component designed for the segmentation and recoloring radiance fields, is capable of dynamically and finely adaptively regulating the feature flow.
Specifically, we first employ a computationally efficient two-layer KAN as the backbone network. This backbone is designed to capture critical geometric structures and appearance representations from the input data a limited computational cost, thereby extracting the backbone non-linear features fn- Subsequently, to achieve fine-grained control over the feature flow, we designed gating module. This module internally embeds a linear layer and a KAN layer, which are utilized to learn a context- dependent gating vector fy: for dynamically modulating the feature flow. However, considering the inherent heterogeneity among different feature channels, a singular f, exhibits limitations in channel- specific regulation. To address this, we designed trainable channel-wise adaptive operator 3 E R 256 This operator performs wise selective activation and suppression on fg. thereby empowering the network with the capability to differentially process distinct semantic channels. Furthermore, shallow features fy, extracted by the linear layer within the gating module, are combined with learnable modulation factor a E R and injected into the main feature flow. This approach aims to enhance training stability and convergence speed. Ultimately, the update process for the backbone
5

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features can be formalized by the following expression:
f Si) fg SiI, fb.
where the enhanced backbone features fú then concatenated with positional encoding y(x), and fed into an output head, constructed from a single KAN layer, to produce the final segmentation mask color weights.
4.2 Local Color Editing
Segmentation stage. During the geometric modeling stage of NeRF [29]. density and multi-channn features fprior are generated to characterize the scene, with the latter encoding prior information about the structure and appearance of the scene. Building on this foundation, we propose fine- tne-grained KAN-based segmentation radiance fields, which leverage inconsistent and geometrically coarse 2D segmentation masks (typically one for forward-facing scenes and three for 360 scenes) generated by Segment Anything Model (SAM) 19 to generate 3D segmentation results that exhibit refined boundary textures and multi-view consistency.
Specifically, we formalize scene segmentation as a view-invariant spatial mapping function. This function maps positional encodings (xx) and prior features fprior (derived from a pre-trained NeRF) to per-point probability distribution s using a softmax activation:
softmax softmax(Fa(1(1))) f
fprsor))
where F. represents our residual adaptive gating KAN in segmentation stage. The design of Fs aims to ensure that the resulting segmentation exhibits consistent geometric structure.
Following this, and a (obtained from pre-trained NeRF) are integrated via overall fine volumetric rendering to assign semantic label each pixel in the segmentation map S:
Miee Σ
$ arggmax
(I(-eep(-oo0))) 8; where T; exp
&
where Näne denotes the number of fine sampling points, ö; is the distance between sample and sample i+1 and & denotes a class index. Notably, our method supports multi-class reakly-supervised segmentation by simply expanding the channel dimension of the output head. This flexibility also broadens the applicability of our local color editing.
Local Recoloring stage. This stage comprises two essential steps: palette extraction and layer decomposition. In the palette extraction step, directly leveraging spatial-level color distribution for segmentation can result in global color inconsistencies from certain viewpoints, such as the abrupt appearance red elements in a predominantly green scene. To address this, we first perform fine- grained segmentation of the scene and then extract the palette from the segmented objects. thereby ensuring spatial color consistency. The layer decomposition step aims to construct KAN-based recoloring radiance fields, which, through simple dimensional concatenation, can be decomposed into multi-dimensional radiance layers weighted by solid colors to enhance recoloring flexibility.
Unlike other 3D recoloring methods [22] 14 32) that follow the strategy introduced by (34). employ the LoCoPalette [8] approach to one or three segmented foreground objects to derive the palette P {p1, p2...., .... .12....PP,, )N and 2D sparse weight maps W (W1, W2,
WN, WNp]. An output
color C is composited from these components using the expression:
Nr
C
Σ
w.Ps
E W and p: E P
where Ne represents the number of base colors in P. We leverage W as supervisory signals, enabling the generated results to effectively reduce the impact of primary color variations on non-primary colors. Additionally, background regions learn to select only black base color, effectively isolating them from the recoloring process.
The local recoloring stage requires the viewing direction 7dd as an additional input, because lighting conditions and specular reflection intensity vary across different viewpoints. this end, recoloring radiance fields learn a mapping from 7(x), 7(d). and fprior to a per-point weight vector wr:
Fu ()(x), r(d), f fprior) + w.
(10)

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Scenes
Annotations
LARNeRF SA3D
KaRF
Fern
Bonsai
Figure 4: Qualitative comparison with NeRF segmentation methods. Zoom-in views are highlighted within the yellow boxes.
where F. represents our residual adaptive gating KAN in local recoloring stage.
Subsequently, we synthesize the RGB image by employing a volume rendering formula analogous to Eq. (8) along with Eq. (9). We observe that, although the global color scheme of local objects remains consistent, color inaccuracies persist due to variations in specular reflection effects (e.g.. the
distinction between deep blue and light blue). Therefore, propose adaptive reconstruction loss, which mitigates the impact of viewpoint-indun color discrepancies:
Σ ( P PER
Callett
(11)
where R represents the sampled rays within a training patch, Cgt denotes the RGB ground truth of the segmentated scene, (r) is the predicted view-dependent weight for ray and P is the learnable view-invariant palette initialized by P. This design assigns the task of learning all corresponding diffuse, highlight, and shadow effects to the weights (r)). Our palette P, in addition to providing the base colors, also allows for the input randomly initialized colors for greater flexibility.
Based on this strategy, the generated weights exhibit multi-view consistency. Therefore, by only directly composite locally recolored scenes. Subsequently,
modifying the trained palette, the masks obtained during the segmentation stage are utilized to directly replace the corresponding regions in the rendered views, thereby achieving faithful restoration of the scene. For scenes with multiple classes, a single, shared palette and unified weight maps are used. The distinct color of each object is then controlled by its specific learned weights within the unified weight maps.
4.3 Optimization
Segment Loss. We compute the multi-class cross-entropy loss between the predicted segmentation map S' and the 2D segmentation map S' generated by SAM [19 to encourage consistency between the rendered segmentation objects and the provided segmentation map terms of the primary object. Here, 1 < I N₁. denotes the class index:
No Σ Σ PER
Lsey
log((()))))
(12)
Weight Loss. We calculate the L2 difference between the predicted weights W and the 2D sparse weights W to learn the main distribution of the weights, gradually optimizing the prominence of different colors within the scene objects:
Ewiight
(13)
PER
Total Loss. We optimize the overall pipeline by jointly minimizing the two losses mentioned above along with the palette-adaptiv reconstruction loss:
LTotal Aseg AEdin (ApCratone Aw Lweight);
(14)
7

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Scenes
PaletteNeRF
ICENeRF
LARNNRF
KaRF
Hordog
H
Fi
Homs
Kithhen
Figure 5: Qualitative comparison with NeRF local color editing methods. The red boxes show the emorr of the comparison methods on the background.
Table 1: Quantitative comparison with NeRF segmentation methods.
MVSeg
30
SA3D
7
KaRF
Scenes
IoU(%)
Ac
IoU(%))
Acc(%)y
IoU(%)
Acc(%)
Orchids
92.7
98.8
87.9
97.8
93.2
98,9
Leaves
94.9
99.7
97.5
99.9
97.2
99.9
Fern
94.3
99.2
97.3
99.6
97.6
99.7
Room
95.6
99.4
90.4
98.6
98.3
99.8
Horns
92.8
98.7
95.4
99.2
95.4
99.2
Fortress
97.7
99.7
98.4
99.8
98.2
99.7
Fork
87.9
99.5
89.8
99.6
84.4
99.3
Truck
85.2
95.1
96.1
98.7
96.9
98.9
Lego
74.9
99.2
90.9
99.7
91.3
99.7
Mean
90.7
98.8
93.7
99.2
94.7
99.5
where Asog and Apdit are set to zero during different stages, while Ap and Aw are hyperparameters for the loss weights.
5 Experimental Results
Datasets. We test KaRF on three different types of datasets: NeRF-Synthetic [29]. 360 bounded synthetic dataset; Mip-NeRF 360 (T). 360 unbounded real-world dataset; and LLFF (28 bounded real-world forward-capture dataset.
Implementation Details. In the geometric modeling stage, we use the Adam optimizer with learning rate of 5e-5, updating based on 2048 rays per iteration, for total of 120k iterations. For other stages, we use learning rate of 5e-4 with 1024 rays per update. The segmentation stage involves 2k iterations, while local recoloring stage involves learnable palette P is trained only during the final 2k iterations. The hyperparameters Ap and Aw are set to and 1c-1, respectively. All experiments are conducted on a single Nvidia RTX 4090 GPU.
iterations, during which the
5.1 Qualitative Evalution
Segmentation. By providing rough reference views of the target object, our proposed KaRF approach can generate fine-grained novel view masks. As illustrated in Figure 4 we compare the proposed KaRF with SA3D 7 and LAENeRF 32 methods in both forward-facing and 360" scenarios, where the second column represents our one or three annotated reference views. The comparison shows that LAENeRF often includes unnecessary objects, as indicated by the red box. Although SA3D shows some advantages in modeling local features, it still struggles with capturing fine texture details of objects, as shown in the yellow box. Additional details are available in the supplementary material.
a comparison of our proposed KaRF method with LAENeRF 32). ICENeRF (3) and PaletteNeRF [2 equipped with the LSeg module (24 across three different datasets. It is worth noting that the results of ICENeRF are taken from its publication 23 From the experimental results, is evident that our proposed KaRF method can accurately change the color of the segmented regions without introducing unnecessary color bleeding or artifacts beyond the segmentation boundaries. For example, the kitchen scene, when recoloring the lego, ICENeRF and PaletteNeRF exhibit noticeable artifacts in the tabletop area. Meanwhile, LAENeRF employs
Local Recoloring,

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Table 2: Quantitative comparison with NeRF local color editing methods.
PNF(with LSeg) [22
ICENeRF [23
LAENeRF [22
KaRF
Datasets
MSEL
MSEL
MSE
MSEJ
I.FFF [28
0.0056
0.0075
0.0020
0.0(42
Blender 29
0.0031
0.0030
0.0003
Mip360 [
0.0027
0.0033
0.0002
Mean
0.0038
0.0075
0.0028
0.0002
Table 3: Quantitative comparison of short-range and long Lang-range consistency after local recoloring.
PNF(with (Seg) [22]
LAFNNRF [2
KaRF
Consistency
Dataset
LPIPST
RMSE
LPIPS..
RMSE]
IPIPS.
RMSE
LLFF 28
0.115
0.066
0.110
0.061
0.100
0.058
Blender 29
0.206
0.239
0.209
0.238
0.201
0.234
Short-range
Mip360 1
0.228
0.103
0,228
0.095
0.218
0.090
Mean
0.183
0.136
0.182
0.131
0.173
0.127
LLFF 22
0.235
0.161
0.233
0.147
0.227
0.145
Blender 29
0.353
0.358
0.356
0.380
0,350
0.361
Long-range
Mip360 [
0.529
0.266
0.541
0.253
0.508
0.243
Mean
0.372
0.262
0.377
0.260
0,362
0,250
Visual Quality
65.7%
14.0%
20.3%
Multi-view Consistency
37.9%
27.7%
34.4%
Ours
PaletteNeRF
LAENeRF
Figure
User study results.
a voxel-based segmentation approach, which generates artifacts near the lego, as shown in the red box. Moreover, proposed KaRF approach excels in maintaining the consistency and plausibility of color adjustments. In the horns scene, our proposed approach better preserves the gloss and tone transitions of the material. In contrast, LAENeRF, due its limited base colors, modifies similar but distinct colors simultaneously, as it cannot achieve sparse weight decomposition. Other competing methods often result in color deviations after recoloring, especially in reflective areas.
5.2 Quantitative Evalution
Segmentation. We follow SA3D 7 to the weakly-superve dataset provided by SPIn-NeRF
30 to compare our proposed KaRF approach with the MVSeg module SPIn-NeRF and the SA3D. As shown in Table T KaRF achieves the highest average Intersection over Union (IoU) and
Accuracy (Acc) across all scenes. This demonstrates the superior segmentation capabilities and robust understanding of scene geometry achieved by model. For a fair comparison, we follow the SPIn-NeRF benchmark protocol, and the values of SPIn-NeRF and SA3D are taken from 7
Local Recoloring. To measure unintended changes in non-edited regions, similar to LAENeRF we compute the mean squared error (MSE) by comparing these regions in the original image against their state after recoloring, In Table 2 present three scenes from three different datasets and compare our proposed KaRF approach with ICENeRF [23], LAENeRF 32] and PaletteNeRF [2] equipped with the LSeg module [24] It is worth noting that the values of ICENeRF the LLFF dataset are from its original publication 23 and thus, the foreground mask we use consistent with that of ICENeRF. Experimental results demonstrate that approach outperforms other competing methods. This superiority arises from our direct use precise masks from the segmentation stage to extract palettes and weights for specific local regions, thereby effectively shielding non-segmented areas from being altered during the recoloring process.
22
To evaluate multi-view view consistency, select pairs of views with intervals of and 7 under short- range and long-range conditions, respectively. Table presents the LPIPS (36) and RMSE results across various scenes after local recoloring with KaRF, PaletteNeRF [22] with the LSeg module 24] and LAENeRF 32]. As can be seen, KaRF achieves the best performance in terms of consistency.
5.3 User Study
We conduct a user study comparing our method with PaletteNeRF 22 and LAENeRF 32 Forty- four participants are presented with pairs of recolored images and videos and asked to make selections

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GT
MLP
B-Spline KAN
GRBFKAN
Kitchen
Bonsai
Figure 7: The impact of GRBFKAN. We compare the composite results for the palettes and weights generated by MLP, B-Spline KAN, and GRBFKAN. The red boxes highlight the composition differences.
Table 4: The impact of residual adaptive gating KAN structure.
w/o All
w/ Gate
w/o Res
w/ All
Datasets
PSNR1
SSIM
PSNR
SSIM
PSNR1
SSIM
PSNRT
SSIM
LLFF 28
32.77
0,961
33.24
0.964
33,50
0.966
33.52
0,966
Blender 29
37.54
0,960
38.30
0,980
38.68
0.984
38.95
0,988
Mip360 ||
30.81
0.931
30.79
0.937
31.02
0.948
31.63
0,952
Mcan
33.71
0.951
34.11
0.960
34.40
0.966
34.70
0.969
GT
w/o LPalette
w/ Paalette
Lego
Figure 8: The impact of L The red boxes highlight the composition differences.
based on visual quality and view consistency. A total of 528 votes collected, as shown in Figure The results indicate clear preference for our method, which is consistently rated as more visually consistent and for its higher quality of local color editing.
5.4 Ablation Studies
Impact of GRBFKAN. Figure 7 shows GRBFKAN outperforming B-Spline KAN and MLP in palette and weight generation and composition. Unlike MLP, which tends to learn global patterns while ignoring local colors, and B-Spline KAN, which tends to smooth out sharp color details, GRBFKAN learns richer color gradations from identical spatial-level palette and weight inputs, yielding compositions more faithful the original scenes.
Impact of Residual dapptiv Gating KAN. We compare the local recoloring results of stacking only KAN layers (w/o All), introducing gating KAN alone (w/ Gate), introducing gating and the adaptive operator G without residual connections (w/o Res), and including all components (w/ All), as shown in Table 4 The results demonstrate that our complete structure achieves the best performance in terms of both PSNR and SSIM. Furthermore, the overall structure with residual connections also demonstrates significant improvement terms of convergence speed and loss.
Impact of Cratette It can be observed in Figure g that the model without the palette-adaptive reconstruction loss (w/o Lptenne) exhibits noticeable deviation in the color reproduction of local regions when compared to the original scene.
6 Conclusion
We have proposed KaRF, a novel weakly-supervised NeRF local color editing method that leveraged the KAN. By designing unified two-stage framework, our proposed KaRF approach effectively achieved both scene segmentation and realistic editing functionalities. Innovatively, we have in- troduced KAN-based task-specific radiance fields and designed the residual adaptive gating KAN structure, which significantly enhances the computational efficiency and accuracy the model. Extensive experimental results have demonstrated that the proposed KaRF algorithm outperforms many state-of-the- methods for segmentation and local recoloring tasks in terms of both visual performance and quantitative metrics.
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Acknowledgments and Disclosure of Funding
This research is supported in part by National Natural Science Foundation of China under Grant 62471199, 62020106011, 1, and 62271414, in part by National Foreign Experts Program under Grant $20250222, in part by National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas), and in part by National Key R&D Program Project under Grant 2033YFC3806003.
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Reviews:
[{"review_id": "maLbn6ApP7", "reviewer": "Reviewer_TYu2", "summary": "The paper introduces a new method for palette-based recoloring of NeRFs. The authors introduce the KAN structure to refine the initial semantic segmentations obtained from SAM, obtaining segmentations of high quality. The resulting regions are used to build a hierarchy of masks, palettes and mixing weights following the LoCoPalettes paper. Results show better consistency and user control than previous works.", "questions": "The method is sound, presented and evaluated properly. More than a question, it is good to see existing methods for 2D to be repurposed for NeRFs in such a clean and successful way.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths:\n- New method for intuitive recoloring of NeRFs, with results of better quality than previous works.\n- Introduction of the KAN network for the mask refinement and recoloring task.\n- Clarity of exposition and evaluation.\n\n- Weaknesses:\n- Smart combination of existing work, but with great results.\n- Processing times seem too high for becoming practical.", "was_revised": true, "final_justification": "I’m keeping my recommendation for acceptance based on my discussion, a the ones the authors had with the other reviewers, who increased their scores after very detailed discussions too", "rebuttal": "Thank you for your positive feedback on our paper. We will now provide our responses to the weaknesses and questions you identified.\n\n**Q1**: Smart combination of existing work, but with great results.\n\n**Response**: Thank you for recognizing the strength of our approach. While we build on powerful techniques from multiple domains, our contribution lies in their careful integration, which is tailored for local color editing in neural radiance fields. Specifically, our two-stage KAN-based architecture, with its residual adaptive gating design and palette-based loss, brings notable improvements in segmentation precision and local recoloring quality. We appreciate your positive feedback on our results.\n\n**Q2**: Processing times seem too high for becoming practical.\n\n**Response**: Thank you for the reviewer’s insightful comment regarding the processing time. We agree that efficiency is crucial for practical applications, and we address this concern from the following perspectives:\n\n1. Trade-off Between Quality and Speed:\nKaRF is designed to prioritize high-fidelity editing, multi-view consistency, and fine-grained control. This naturally incurs higher computational cost compared to some faster methods (e.g., LAENeRF, IReNe), but yields superior editing quality, as shown in Figure 5 (main paper) and Figure 4 (supplementary material). We believe this trade-off is justified, especially for applications demanding precision.\n\n2. Source of Computational Overhead:\nThe primary cost arises from using Kolmogorov-Arnold Networks (KANs), which offer significantly greater nonlinear expressiveness than MLPs. As validated in our ablation studies, this contributes directly to better segmentation and local recoloring performance, albeit with added computation.\n\n3. Reasonable Compared to SOTA Methods:\nWhile our method takes longer than LAENeRF, its processing time is favorable when compared to other SOTA editing methods. KaRF is faster than both PaletteNeRF and the recent 3DGS-based method ICE-G. This indicates that the processing time of KaRF is within a reasonable range, offering a practical balance between speed and quality.\n\n4. Potential for Acceleration:\nKaRF is flexible and allows for quality-speed trade-offs. By reducing the local recoloring iterations from 3k to 2k or 1k, we can significantly reduce runtime (down to ~6.5 min) while maintaining respectable quality, as shown in Tab 1. This suggests good potential for deployment in semi-interactive scenarios.\n\n**Tab 1**: Quality-speed trade-off (PSNR↑ SSIM↑).\n|Dataset/Iterations (time)|1k (3.5min)|2k (6.5min)|3k (10min)|\n|-|-|-|-|\n|LLFF|31.49 0.946|32.58 0.961|33.52 0.966|\n|Blender|31.33 0.967|34.42 0.979|35.12 0.985|\n|Mip360|26.15 0.914|29.82 0.939|31.63 0.952|\n\n**Q3**: The method is sound, presented and evaluated properly. More than a question, it is good to see existing methods for 2D to be repurposed for NeRFs in such a clean and successful way.\n\n**Response**: We are truly grateful for such a positive and encouraging assessment of our work. We are delighted that you found our method to be sound and presented and evaluated properly. Your observation perfectly captures our core motivation: to repurpose powerful, existing 2D methods for NeRFs in a clean and successful way. We hope our method can build a bridge between mature 2D technologies and emerging 3D representations."}, {"review_id": "hVB3hjWQa7", "reviewer": "Reviewer_aRt4", "summary": "The paper introduces KaRF (Kolmogorov-Arnold Networks-based Radiance Fields), a novel weakly-supervised method for local color editing of neural radiance fields (NeRF).\n\nThe first major contribution is the overall KaRF framework architecture. It introduces a two-stage approach that combines segmentation and local recoloring while requiring minimal guiding information for selective recoloring of arbitrary regions. This design achieves multi-view consistency and precise boundary control, addressing key limitations of existing approaches.\n\nOn the technical side, the paper makes several innovative contributions. It proposes a novel \"residual adaptive gating KAN structure\" that enhances segmentation accuracy and editing refinement. The authors introduce a key modification to vanilla KAN by replacing B-spline functions with Gaussian Radial Basis Functions (GRBFs), which better handle high-frequency spatial details and sharp color transitions. They also introduce a palette-based color-adaptive loss to improve color mixing accuracy.\n\nIn terms of performance, KaRF demonstrates superior results compared to state-of-the-art methods both qualitatively and quantitatively. The approach shows better handling of color bleeding issues and boundary precision compared to existing approaches.", "questions": "Please comment on the limitations raised above.", "limitations": "Limitations are discussed in the supplementary materials. The reviewer wonders if this is an acceptable practice, as key limitations should be presented in the main paper.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nTechnical Innovation and Soundness:\nThe paper introduces several novel technical contributions that build meaningfully on prior work. The substitution of B-splines with GRBFs in the KAN framework is well-justified, as it better handles high-frequency spatial details and sharp color transitions. The residual adaptive gating KAN structure shows thoughtful design in addressing training stability while maintaining expressive power.\n\nClear Problem Motivation:\nThe authors clearly articulate the limitations of existing NeRF-based editing approaches, particularly around boundary precision, multi-view consistency, and color bleeding. The proposed solution directly addresses these identified gaps in current methods.\n\nComprehensive Evaluation:\nThe evaluation is thorough, testing the method across multiple datasets (NeRF-Synthetic, Mip-NeRF 360, LLFF) and comparing against relevant state-of-the-art methods. The inclusion of both qualitative and quantitative metrics, along with user studies, strengthens the validation of their claims.  \n\nLimitations\n\nLimited Analysis of KAN Modifications:\nOne of the paper's most significant contributions is the novel use of KAN for NeRF editing and its modification to incorporate Gaussian RBFs instead of B-splines. However, the analysis of this crucial modification is insufficiently thorough. The main paper only provides one anecdotal comparison in Figure 7, while the supplementary material's Table 6 only shows results for a single scene (kitchen). A more rigorous analysis across multiple scenes and different types of editing operations would be valuable to understand when and why GRBFs outperform B-splines. This would help readers better understand the trade-offs and benefits of this fundamental architectural choice.\n\nComputational Efficiency:\nThe method requires approximately 10 minutes for processing, which is almost twice as slow as LAENeRF and significantly slower than other recent approaches. For instance, IReNE (Mazzucchelli et al., 2024) achieves similar editing capabilities in about 5 seconds. This performance gap could limit the method's practical applicability, particularly in interactive editing scenarios where quick feedback is essential. The paper would benefit from a discussion of potential optimizations or trade-offs between quality and speed.\n\nWhile the paper claims superior segmentation performance, the actual improvements over existing methods are relatively modest. As shown in Table 1, KaRF achieves an IoU of 94.7% and accuracy of 99.5%, which represents only a small improvement over SA3D (93.7% IoU, 99.2% accuracy) and MVSeg (90.7% IoU, 98.8% accuracy).", "was_revised": true, "final_justification": "I acknowledge the authors' detailed responses to my review comments. Their analysis of the GRBF-based KAN modifications adequately addresses one of my primary concerns through experimental validation across multiple scenes and metrics.\n\nThe authors have also satisfactorily addressed my concerns regarding computational efficiency and segmentation performance. Their quality-speed tradeoff analysis provides useful insights for practical implementations across different use cases.  \n\nBased on these responses, I am satisfied that the authors have addressed the concerns raised in my review and  I maintain my recommendation for acceptance of this paper.", "rebuttal": "Thank you for your detailed review of our work. We have carefully considered your comments and offer our responses below.\n\n**Q1**: Limited Analysis of KAN Modifications.\n\n**Response**: We thank the reviewer for highlighting the importance of thoroughly analyzing our GRBF-based modification to KAN. We agree that this is a central contribution and have conducted additional experiments to provide a more rigorous evaluation.\n\nExpanded Evaluation:\nWe systematically compared GRBFKAN with both B-Spline KAN and MLP across six representative scenes (two per dataset) and two key tasks: local recoloring and segmentation.\n\n1. Local Recoloring:\nAs shown in Tab 1, GRBFKAN consistently achieves the best PSNR and SSIM across all scenes. This supports our claim that GRBFs, with their flexible locality and sharper kernel responses, are better at capturing the high-frequency details present in colors and textures than smoother B-splines or global MLPs. Additionally, GRBFKAN exhibits faster and more stable convergence (see supplementary material Figure 7).\n\n**Tab 1**: Impact of the GRBFKAN (PSNR↑ SSIM↑).\n|Scene|MLP|BspineKAN|GRBFKAN|\n|-|-|-|-|\n|LLFF-Horns|35.14 0.971|35.66 0.968|**35.97 0.977**|\n|LLFF-Flower|32.13 0.964|32.94 0.966|**33.52 0.966**|\n|Blender-Chair|33.76 0.986|35.07 0.984|**35.12 0.985**|\n|Blender-Hotdog|36.49 0.967|36.85 0.972|**38.95 0.988**|\n|Mip360-Bonsai|36.96 0.985|36.97 0.977|**38.31 0.989**|\n|Mip360-Kitchen|27.65 0.934|30.29 0.945|**31.63 0.952**|\n|Mean|33.69 0.968|34.63 0.969|**35.58 0.976**|\n\n2. Segmentation:\nAs shown in Tab 2, GRBFKAN consistently outperforms both B-Spline KAN and MLP in average IoU and Acc. This suggests its improved ability to learn geometric and semantic boundaries, which is critical for high-quality editing. Furthermore, the loss convergence plots (see supplementary material Figure 7) reinforce this advantage, showing that GRBFKAN not only converges faster but also achieves a significantly lower final loss.\n\n**Tab 2**: Impact of the GRBFKAN (IoU↑ Acc↑).\n|Scene|MLP|BspineKAN|GRBFKAN|\n|-|-|-|-|\n|LLFF-Horns|0.949 0.992|0.946 0.991|**0.951 0.992**|\n|LLFF-Flower|**0.961 0.991**|0.960 0.991|0.960 0.991|\n|Blender-Chair|0.967 0.997|0.967 0.997|**0.970 0.997**|\n|Blender-Hotdog|0.949 0.994|0.946 0.994|**0.962 0.996**|\n|Mip360-Bonsai|0.862 0.994|0.849 0.993|**0.868 0.994**|\n|Mip360-Kitchen|0.955 0.994|0.952 0.993|**0.961 0.994**|\n|Mean|0.941 0.994|0.937 0.993|**0.945 0.994**|\n\nSummary:\nThese results reinforce that GRBFKAN offers clear advantages, especially in challenging scenarios involving complex textures or precise segmentation. We will integrate this extended analysis into the main paper to better justify our architectural choices.\n\n**Q2**: Computational Efficiency.\n\n**Response**: We appreciate the reviewer's concern regarding the processing time. We agree that efficiency is important, especially for interactive scenarios, and would like to elaborate on this from the following perspectives:\n\n1. Justified Trade-off for Quality:\nKaRF’s relatively longer runtime stems from its use of Kolmogorov-Arnold Networks (KANs), which significantly enhance nonlinear expressiveness. This is critical for achieving superior editing fidelity, multi-view consistency, and fine-grained color control. As shown in Figure 5 (main paper) and Figure 4 (supplementary material), KaRF reduces artifacts like color bleeding, which are often observed in faster methods such as LAENeRF and IReNE.\n\n2. Favorable Comparison with SOTA Methods:\nWhile slower than LAENeRF, KaRF is faster than other SOTA editing methods such as PaletteNeRF and the recent 3DGS-based method ICE-G. This positions it as a competitive choice when editing quality is prioritized. Meanwhile, this indicates that KaRF's processing time is within a reasonable range, offering a practical balance between speed and quality.\n\n3. Quality-Speed Trade-off:\nAs shown in Tab 3, We conduct additional experiments to demonstrate tunable speed-quality trade-offs. Reducing the local recoloring stage from 3k to 2k iterations shortens runtime to ~6.5 minutes with only marginal PSNR/SSIM drops. This makes KaRF viable for semi-interactive use cases.\n\n**Tab 3**: Quality-speed trade-off (PSNR↑ SSIM↑).\n|Dataset/Iterations (time)|1k (3.5min)|2k (6.5min)|3k (10min)|\n|-|-|-|-|\n|LLFF|31.49 0.946|32.58 0.961|33.52 0.966|\n|Blender|31.33 0.967|34.42 0.979|35.12 0.985|\n|Mip360|26.15 0.914|29.82 0.939|31.63 0.952|\n\n**Q3**: The improvement in segmentation performance is relatively limited.\n\n**Response**: Thank you for this insightful question regarding our segmentation performance. We agree that when looking solely at the average metrics in Table 1 (main paper), the quantitative gains from KaRF may seem modest.\n\nHowever, we emphasize that in the context of local color editing, the visual quality and boundary sharpness of the segmentation is far more impactful than marginal gains in overall averages. As shown in Figure 4 (main paper), SA3D often yields blurred or imprecise boundaries, while KaRF produces sharper, texture-preserving masks—essential for preventing color bleeding.\n\nCrucially, segmentation in our framework is not an end goal but a critical intermediate step for enabling high-fidelity local editing. It is this high-precision segmentation that allows KaRF to perform nearly perfect, isolated recoloring. The profound downstream impact of this precision is quantified in Table 2 (main paper): the MSE in non-edited regions for our method is an order of magnitude lower than that of all competing methods. This dramatic reduction in error directly stems from our accurate boundaries, which prevent the color bleeding that plagues other approaches.\n\nIn summary, while the metric deltas are small, their downstream impact on editing quality is substantial and meaningful.\n\n**Q4**: Limitations are discussed in the supplementary materials.\n\n**Response**: Thank you for raising this important point. We completely agree with your assessment. In preparing the initial manuscript, due to a strict page limit, we had to make difficult decisions regarding content placement. To dedicate more space in the main paper to our core methodology and experimental results, we moved the discussion on limitations, certain finer experimental details, and additional visualizations to the supplementary material. And we commit to moving the limitations section from the supplementary material back into the main paper. This change will ensure that all readers can easily access our discussion on the scope of the work and potential avenues for future research."}, {"review_id": "IFTQOHoRMB", "reviewer": "Reviewer_1rRd", "summary": "This paper introduces an alternative backbone for NeRF by replacing the MLP with a KAN-based network for local color editing. Given the flexible activation prediction of KAN the authors aim to improve segmentation with finer edges and more precise color editing than MLP based NeRF. The method however added architectural complexity significantly, and the authors did not specify the runtime of the model but simply state certain design to faciliate the computational overhead. Additionally, the method lacks rigorous analysis of scalability and training stability beyond the shown dataset examples.", "questions": "See weakness.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The given implementation details are not sufficient to reproduce the method without the release of the codebase. The author also did not specify whether they plan to release the code. \nThe authors did not specify how many datasets were used for experiments, but merely explained the number of types of scenes. The authors also did not specify the change in implementation for different types of NeRF scenes. \nThe runtime comparison is not extensive and significant enough through simply providing \"roughly 10 min\" without specifying the type of scenes. \nThe use of limited cross-dimensional interaction by KAN can create interdependencies between view directions and 3d locations compared to fully connected MLPs. How does the model handle the view-dependent effects such as specularity or transparency in editing? How would the model behave for scenes with complex lighting? \nOverall, the reason to adapt KAN for the specific task of localized NeRF recoloring is not well conveyed through the writing of this paper, and the authors have not shown enough results to demonstrate superiority of the method.", "was_revised": true, "final_justification": "I acknowledge the effort put in by the authors to address my concerns. I update my rating to borderline accept.", "rebuttal": "Thank you for the questions you have raised. We will address each of them in turn below.\n\n**Q1**: The given implementation details are not sufficient to reproduce the method without the release of the codebase. The author also did not specify whether they plan to release the code.\n\n**Response**: We sincerely apologize that the details in our paper and supplementary material were not sufficient to fully address your concerns about reproducibility.\n\nWe endeavored to provide the key information required to reproduce our method. To be as clear as possible, we have detailed the following in the Implementation Details subsection of section 5: The optimizer used for all stages. The learning rates and batch sizes for the different training stages. The specific number of iterations for each training stage. The weight hyperparameters for all loss functions. Furthermore, in section 4, we provided a detailed network architecture diagram (Figure 3), which includes our core residual adaptive gating KAN structure. We also supplied the precise mathematical definitions for all loss functions and the equations for segmentation and local recoloring. We believe this information provides a solid foundation for reimplementation.\n\nMost importantly, we want to state our open-source commitment unequivocally. As mentioned in our response to the NeurIPS checklist 5, we will make our work fully accessible. Upon acceptance of the paper, we will immediately release the complete source code, all pre-trained models, and the pre-processed data to ensure full reproducibility.\n\n**Q2**: The authors did not specify how many datasets were used for experiments and the change in implementation for different types of NeRF scenes.\n\n**Response**: We apologize for any lack of clarity in our paper. Regarding the datasets used, we would like to clarify that we specified the three distinct datasets in the datasets subsection of section 5. The datasets are: NeRF-Synthetic (Blender), Mip-NeRF 360, and LLFF. For our qualitative evaluation (Figure 5), we selected one representative scene from each of these datasets. Similarly, our quantitative evaluation (Table 2) was conducted on a total of nine scenes drawn from across these same three datasets to ensure a comprehensive assessment.\n\nRegarding your second point on implementation changes for different NeRF scene types, we thank you for raising this. We provided a single, unified set of implementation details because our proposed KaRF framework demonstrates excellent robustness and generalization. The same set of hyperparameters and training configurations was applied across all three distinct dataset types without needing any scene-specific or dataset-specific tuning. For the NeRF-Synthetic, LLFF, and Mip-NeRF 360 datasets, the learning rates, iteration counts, and loss weights listed in the implementation details subsection were used consistently.\n\n**Q3**: The runtime comparison is not extensive and significant enough through simply providing \"roughly 10 min\" without specifying the type of scenes.\n\n**Response**: We apologize if our description of the runtime was not sufficiently detailed. We chose the Fern scene for our exemplary comparison, a practice aligned with other methods such as LAENeRF, to provide a fair time comparison on a common and publicly available benchmark.\n\nMore importantly, we wish to clarify that the runtime for our method is highly stable and consistent across different scenes and dataset types. This is because the total processing time is primarily determined by the fixed number of training iterations and the number of rays processed per batch, rather than the visual complexity of the content of the scene. In our experimental setup, the training iterations and other settings are kept constant for all datasets, including NeRF-Synthetic, LLFF, and Mip-NeRF 360. As a result, the time required for local color editing remains nearly identical regardless of the scene. To provide concrete evidence, Tab 1 shows the processing times for several representative scenes from different datasets:\n\n**Tab 1**: Processing Times.\n|Dataset|Time|\n|-|-|\n|LLFF|9.93min|\n|Blender|9.97min|\n|Mip360|10.14min|\n|Mean|10.01min|\n\n**Q4**: How does the model handle the view-dependent effects such as specularity or transparency in editing? How would the model behave for scenes with complex lighting?\n\n**Response**: While individual edges in a KAN learn univariate functions, we would like to clarify that this does not preclude the network as a whole from learning complex, cross-dimensional dependencies, such as those between 3D locations and view directions. Our model effectively handles view-dependent effects through the following mechanisms:\n\n1. Explicit View Direction Input: In the local recoloring stage, which is responsible for effects like specularity and transparency, we provide the view direction as an explicit input to the network, following the successful practice of the original NeRF. This is formally shown in Equation (10) of our paper.\n\n2. Expressive Power of the Composed Network: Although each edge activation is a univariate function, the full network, through its multi-layer composition, is fully capable of learning complex mappings from high-dimensional inputs to high-dimensional outputs. Our proposed residual adaptive gating KAN structure is designed to learn these non-linear dependencies between position and view direction effectively.\n\n3. Qualitative Evidence: Our experimental results demonstrate the effectiveness of this design. In the Horns scene in Figure 5 of the main paper, our method successfully preserves the surface gloss and tonal transitions after changing the color of the horns. This gloss is a classic view-dependent effect, and its preservation proves that our model accurately learned and reproduced this dependency, a task where other methods like LAENeRF show visible artifacts.\n\nRegarding the behavior of our model in scenes with complex lighting: The Mip-NeRF 360 dataset is representative of the scenes you describe, containing unbounded, real-world environments with complex lighting, shadows, and reflections. Our method achieves excellent editing results across multiple scenes from this challenging dataset, such as the Kitchen scene (main paper Figure 5) and the Bonsai scene (supplementary material Figure 11). In these scenarios, KaRF not only accurately replaces the color of the target object but also naturally integrates the new color into the existing lighting environment of the scene, correctly rendering the resulting highlights, shading, and shadows on the surface of the object.\n\n**Q5**: The reason to adapt KAN for the specific task of localized NeRF recoloring is not well conveyed through the writing of this paper, and the authors have not shown enough results to demonstrate superiority of the method.\n\n**Response**: We sincerely apologize that our initial manuscript did not sufficiently convey the core motivations for choosing the KAN architecture. We appreciate you raising these critical points, and we hope to offer a much clearer justification here. Our primary reasons for adapting KAN for the specific task of localized NeRF recoloring are as follows:\n\n1. Superior Non-linear Expressiveness: Local color editing is an inherently complex function-fitting problem. KANs possess stronger function approximation capabilities and greater expressive power than traditional MLPs, making them better suited for this task.\n\n2. Precise Modeling of High-Frequency Details: We further modified the KAN architecture to use GRBF instead of B-splines. This decision was driven by the fact that local editing often involves high-frequency details like specular highlights and sharp edges. Compared to the smoothing properties of B-splines, GRBFs can more flexibly adapt to and reconstruct these rapidly changing local features.\n\n3. Higher Training Efficiency and Stability: Figure 7 in our supplementary material provides empirical evidence for the advantages of GRBF KAN. In both the segmentation and local recoloring stages, the training loss for GRBF KAN is consistently lower and converges faster than that of both MLP and B-Spline KAN architectures.\n\nTo address your valid concern about the lack of sufficient results, we have conducted a broad set of new quantitative experiments to systematically demonstrate the superiority of our GRBF KAN architecture. We selected two representative scenes from each of our three datasets and performed a comprehensive comparison between GRBF KAN, B-Spline KAN, and MLP.\n\n1. Local Recoloring Task: We evaluate the reconstruction quality after palette synthesis. The results in Tab 2 show that GRBFKAN outperforms B-Spline KAN and MLP across all tested scenes in terms of PSNR and SSIM.\n\n**Tab 2**: Impact of the GRBFKAN (PSNR↑ SSIM↑).\n|Scene|MLP|BspineKAN|GRBFKAN|\n|-|-|-|-|\n|LLFF-Horns|35.14 0.971|35.66 0.968|**35.97 0.977**|\n|LLFF-Flower|32.13 0.964|32.94 0.966|**33.52 0.966**|\n|Blender-Chair|33.76 0.986|35.07 0.984|**35.12 0.985**|\n|Blender-Hotdog|36.49 0.967|36.85 0.972|**38.95 0.988**|\n|Mip360-Bonsai|36.96 0.985|36.97 0.977|**38.31 0.989**|\n|Mip360-Kitchen|27.65 0.934|30.29 0.945|**31.63 0.952**|\n|Mean|33.69 0.968|34.63 0.969|**35.58 0.976**|\n\n2. Segmentation Task: As shown in Tab 3, GRBFKAN consistently outperforms both B-Spline KAN and MLP in average IoU and Acc. This provides strong empirical support for our claim that GRBFKAN possesses a superior capacity for learning scene geometry and precise semantic boundaries.\n\n**Tab 3**: Impact of the GRBFKAN (IoU↑ Acc↑).\n|Scene|MLP|BspineKAN|GRBFKAN|\n|-|-|-|-|\n|LLFF-Horns|0.949 0.992|0.946 0.991|**0.951 0.992**|\n|LLFF-Flower|**0.961 0.991**|0.960 0.991|0.960 0.991|\n|Blender-Chair|0.967 0.997|0.967 0.997|**0.970 0.997**|\n|Blender-Hotdog|0.949 0.994|0.946 0.994|**0.962 0.996**|\n|Mip360-Bonsai|0.862 0.994|0.849 0.993|**0.868 0.994**|\n|Mip360-Kitchen|0.955 0.994|0.952 0.993|**0.961 0.994**|\n|Mean|0.941 0.994|0.937 0.993|**0.945 0.994**|"}, {"review_id": "RfwLouHxzz", "reviewer": "Reviewer_JVR7", "summary": "This paper addresses the task of color editing in neural radiance fields (NeRFs), with a specific focus on combining local editing and palette-based editing. Traditional palette-based approaches typically lack spatial precision unless paired with segmentation masks. The authors overcome this limitation by using SAM-generated masks to supervise the learning of a local 3D segmentation field, which is then used to perform palette-based decomposition in localized regions.\n\nStarting from a pre-trained NeRF, the method leverages its multi-channel features to train a per-point segmentation based on KAN (upgraded to GRBFKAN), followed by palette decomposition on the segmented regions. The key contribution lies in enabling localized palette decomposition, which allows for richer and more controllable local edits.\n\nThe authors demonstrate improved local editing capabilities compared to a basic KAN-based baseline.", "questions": "How many SAM masks are actually needed for training?\n\nHow is SAM being prompted?\n\nWould it be possible to visualize or quantify the separation between what is modeled as view-independent (via W) and view-dependent (residuals)?\n\nClarification is needed on the input-output relationship between the segmentation map and the palette weights W — specifically, how (and whether) view direction influences both.\n\nIt is unclear whether the parts of the scene not belonging to the segmented object (e.g., background or other classes) are also palette-decomposed. If they are not palette-decomposed, what does KaRF learn for those regions? Just a single base color per pixel/point?\nOr does KaRF learn separate palette decompositions for each segmentation class (e.g., one palette for object 1, another for object 0)?\n\n\nI would be open to accepting this paper if the following conditions are addressed:\n\n-The authors demonstrate that the segmentation module can operate effectively with only a single input mask or single user interaction, and provide both quantitative and qualitative evidence of its generalization capabilities.\n\n-There is clear empirical justification—both qualitative and quantitative—for using KAN or GRBF-KAN over standard MLPs. It should be demonstrated that the improved performance and enhanced local editability are due to the KAN architecture itself, and not simply the result of increased supervision(SAM) compared to other methods.\n\n-The paper includes more comprehensive 360° visual evidence, particularly on the Mip-NeRF 360 dataset, showing that the method maintains multi-view consistency and handles well view-dependent effects.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 1, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Supervision Level and Use of SAM Masks:\nThe method relies on segmentation masks generated by SAM on multiple views to train a 3D segmentation field via a KAN-based radiance field. While the authors frame this as “weak supervision,” the requirement of 2D masks from multiple viewpoints constitutes a considerably stronger form of supervision than prior methods. ICE-NeRF uses sparse scribbles on a single image and IReNe relies only on a single segmentation mask. In contrast, KaRF appears to need multiple SAM masks to function effectively. Additionally, the paper does not clearly state how many views are used — Figure 4 shows two, but it's unclear if that's just for illustration. Calling this “weak supervision” is, in my view, somewhat misleading.\n\nLack of Justification for Using KANs:\nThe paper introduces using KANs, specifically GRBF-KANs, as an architectural innovation for both segmentation and recoloring. While ablations show that their residual adaptive gating improves over basic KANs and B-spline variants, there is no direct numerical comparison with standard MLPs, despite MLPs being used in all previous NeRF-based editing frameworks. Since the architectural shift to KANs is the major contribution, this omission weakens the justification for their use. \n\nViewdep Inconsistency:\nThe paper states that segmentation is view-invariant (Eq. 7), but Figure 3 shows the view direction as an input to the segmentation module, creating an inconsistency. This should be clarified, as it raises doubts about whether the segmentation field truly disentangles geometry from appearance.\n\nMore importantly, I’m confused by the treatment of view direction in the palette weight prediction. In the text, the palette weights W are defined as a function of view direction (Eq. 10). That’s acceptable, although in previous SOTA methods, like PaletteNeRF, the palette weights were view-invariant. In other papers, like IReNe, there was a focus on explicitly dividing specular and diffuse components. Now, if there’s a need to make the weights not be affected too much by view-dependent color, I would have assumed Equation 11 to be used with view-invariant palette weights W — to say: \"learn as much of the color as you can through W, so the view direction can then learn only the residual.\" But that doesn’t make sense if W is not direction-invariant.\nIf W is view-dependent, then Equation 11 is effectively a standard RGB reconstruction loss — so why not call it that? I believe a proper RGB reconstruction loss is actually missing in KaRF (if Equation 11 is not intended for that) to fully reconstruct the scene. I would have expected both a photometric RGB loss and a loss that encourages W to learn as much of the diffuse color as possible.\nAs written, Equation 11 seems like a standard RGB reconstruction loss, and Equation 13 is just to push W to learn the main color components. If that’s the case, I wouldn’t call Equation 11 \"L_palette\" — it’s more accurate to call it \"PhotometricLoss\".\nI would like the authors to clarify this point.\n\nLimited Scope of Ablation Studies:\nMost ablation studies are conducted on single scenes only — e.g., LPalette is tested only on Lego, GRBF-KAN only on Kitchen, and time comparison only on Fern. This narrow scope makes it difficult to assess the generalizability of the architectural improvements or the claimed efficiency. Broader evaluation would strengthen the evidence for the proposed choices.\n\nLimited Qualitative Evaluation on Challenging Scenes:\nI would have appreciated more qualitative results, especially full 360° visualizations on the Mip-NeRF 360 dataset. Since reflections and complex lighting effects are critical challenges in recoloring, it is important to see how well the method performs in those settings. Static frame comparisons are insufficient to assess view consistency in reflective surfaces. I find it suspicious that no complete 360° video of Mip-NeRF 360, or even images showing significant viewpoint changes, were included either in the main paper or the supplementary material. This omission makes me think the method may not generalize well in 360° cases.", "was_revised": true, "final_justification": "I am raising my score to a 3, since they showed evidence of the KAN being more useful than the MLPs. \nStill, I don't like how the ablation studies were done, throughout all the datasets, choosing only a few scenes per dataset, sometimes only one.\n\nAlso, since nothing is done about view-dependent effects but they claim it is learned through the black-and-white palette, I would have liked videos on the Mip360 datasets (the most complex) showcasing how the recoloring is working even in 360° scenarios.", "rebuttal": "Thank you for your thoughtful comments and constructive feedback. We conduct extensive experiments to address your feedback (including excellent results with 1/3 input masks, more comprehensive ablation studies, and 360° videos of mip360). However, due to the official rule against adding images in the rebuttal, we provide ample quantitative results and describe some of the visual outcomes. We promise that if the paper is accepted, we will include more visual experiments.\n\n**Q1**: Supervision Level and Use of SAM? How many masks for training? How to prompt SAM?\n\n**Response**: We define weak supervision by two key properties: the use of sparse and imperfect labels. Our method requires only 1-3 coarse masks per scene (one for forward-facing like LLFF, three for 360° scenes like Mip360). These masks are automatically generated by prompting SAM with points and are used directly without any manual refinement.\n\nThough we may use more than one mask, our supervision is more efficient than prior work. Unlike IReNe, which requires precise manual maps and new reference images for multiple edits, KaRF uses coarse, auto-generated masks and modifies a single learned palette. Furthermore, unlike ICENeRF (often requires at least two masks: foreground and background), whose foreground mask requirement scales with the number of colors being edited, KaRF edits multiple objects within the same sparse set of masks.\n\nWe apologize for the confusion caused by Figure 4. The dense-mask setup shown was used exclusively to adhere to the SPIn-NeRF benchmark protocol for a fair comparison and does not represent our method's standard workflow.\n\n**Q2**: Viewdep Inconsistency? the input-output relationship?\n\n**Response**: We apologize for the lack of clarity in Figure 3. The confusion stems from the legend: the view direction input is used only for local recoloring, not segmentation. Its enclosure in the red dashed box visually signifies its exclusive role in the recoloring process.\n\nTo directly clarify the input-output relationships for each stage: \n\n1. Segmentation Stage (View-Invariant): Inputs: 3D position + prior features. Output: consistent segmentation maps. This stage learns the intrinsic geometry and semantic class of the scene, which is independent of the viewing angle.\n\n2. Local Recoloring Stage (View-Dependent): Inputs: 3D position + view direction + prior features. Output: consistent weight maps and a palette. Composites colors by location and viewing angle to reconstruct view-dependent effects (gloss, shading, reflections).\n\n**Q3**: Clarification on the L_palette and view-dependent/independent effects?\n\n**Response**: Unlike PaletteNeRF/IReNe, KaRF's view-dependent weights W model all color effects (diffuse, specular) with a view-invariant palette P. This is enabled by LoCoPalette (including black/white bases), supporting our two-phase training:\n\n1. First 2k Iterations: Lweight provides a coarse, view-invariant guide to color distribution. Simultaneously, Lpalette (with a fixed palette P) acts as a strong view-dependent loss, enabling W to learn all necessary details for ground truth approximation. This includes utilizing white base for specular highlights and black base for shadows, with all diffuse, highlight, and shadow weights learned within W.\n\n2. Last 1k Iterations: Palette P becomes learnable. The L_palette now also adaptively fine-tunes the base colors in P to achieve the best possible color fidelity for the reconstructed image.\n\nYou have made an excellent point regarding the naming of L_palette. To better reflect its dual role, we will rename it to Palette-Adaptive RGB Reconstruction Loss.\n\nTo visualize and quantify effect separation, our visualizations confirm that diffuse weights stay stable when the viewing angle changes for a fixed 3D point. Conversely, white and black weights vary significantly with view direction, precisely how view-dependent highlights and shadows are formed. An ablation study in Tab 1, with image contrast amplified 4x to highlight specular effects, quantifies this impact, demonstrating significant improvement from view-dependent weights in capturing complex lighting compared to a view-invariant model.\n\n**Tab 1**: Weight W.\n|Type|PSNR↑|SSIM↑|\n|-|-|-|\n|View-Invariant|25.69|0.961|\n|View-Dependent|**27.42**|**0.972**|\n\n**Q4**: Strategy for background or other classes?\n\n**Response**: KaRF is a modular framework that exclusively edits user-specified foreground objects, leaving the background unmodified. During training, background regions learn to select only a black palette base, effectively isolating them from the recoloring process.\n\nFor scenes with multiple classes, a single, shared palette and a unified weight field W are used. The distinct color of each object is then controlled by its specific learned weights within this unified weight field.\n\n**Q5-**: Single input mask evidence?\n\n**Response**: For LLFF, we require only a single mask to achieve high-quality segmentation. For Blender and Mip360, we use three masks from minimally-overlapping viewpoints.\n\nWe performed extensive experiments to validate this capability. The results of Tab 2 show that the metrics achieved with this sparse-mask supervision are nearly identical to the results from using a much larger set of masks. Qualitatively, the generated segmentations and the final local edits are visually indistinguishable from the results obtained with denser supervision. This demonstrates our model's strong generalization ability, allowing it to learn a complete and accurate segmentation field from extremely sparse signals.\n\n**Tab 2**: Sparse masks input (IoU↑ Acc↑).\n|Scene|1/3 masks|all masks|\n|-|-|-|\n|LLFF-Horns|0.951 0.992|0.954 0.992|\n|LLFF-Flower|0.960 0.991|0.963 0.991|\n|Blender-Chair|0.970 0.997|0.982 0.998|\n|Blender-Hotdog|0.962 0.996|0.964 0.996|\n|Mip360-Bonsai|0.868 0.994|0.879 0.994|\n|Mip360-Kitchen|0.961 0.994|0.968 0.995|\n\n**Q6-**: Impact of GRBFKAN, L_palette, residual adaptive gating KAN and time comparison?\n\n**Response**: We sincerely apologize that our initial experiments were not sufficient to fully demonstrate the generality and superiority of our design choices.\n\n1. Impact of GRBFKAN: To demonstrate that our performance gains stem from the KAN architecture itself and not simply the supervision level, we conducted a broad comparison of MLP, B-Spline KAN, and GRBFKAN across six scenes from three datasets. The results of Tab 3 show that GRBFKAN consistently yields the best PSNR and SSIM, confirming its superior ability to model high-frequency color details compared to the smoother B-splines and global MLP. The results of Tab 4 confirm that GRBFKAN achieves the best average IoU and Acc, demonstrating its enhanced ability to learn complex geometric and semantic boundaries. Qualitatively, GRBF KAN also produces sharper segmentation boundaries and more accurate color compositions that are faithful to the original scenes.\n\n**Tab 3**: Impact of the GRBFKAN (PSNR↑ SSIM↑).\n|Scene|MLP|BspineKAN|GRBFKAN|\n|-|-|-|-|\n|Horns|35.14 0.971|35.66 0.968|**35.97 0.977**|\n|Flower|32.13 0.964|32.94 0.966|**33.52 0.966**|\n|Chair|33.76 0.986|35.07 0.984|**35.12 0.985**|\n|Hotdog|36.49 0.967|36.85 0.972|**38.95 0.988**|\n|Bonsai|36.96 0.985|36.97 0.977|**38.31 0.989**|\n|Kitchen|27.65 0.934|30.29 0.945|**31.63 0.952**|\n|Mean|33.69 0.968|34.63 0.969|**35.58 0.976**|\n\n**Tab 4**: Impact of the GRBFKAN (IoU↑ Acc↑).\n|Scene|MLP|BspineKAN|GRBFKAN|\n|-|-|-|-|\n|Horns|0.949 0.992|0.946 0.991|**0.951 0.992**|\n|Flower|**0.961 0.991**|0.960 0.991|0.960 0.991|\n|Chair|0.967 0.997|0.967 0.997|**0.970 0.997**|\n|Hotdog|0.949 0.994|0.946 0.994|**0.962 0.996**|\n|Bonsai|0.862 0.994|0.849 0.993|**0.868 0.994**|\n|Kitchen|0.955 0.994|0.952 0.993|**0.961 0.994**|\n|Mean|0.941 0.994|0.937 0.993|**0.945 0.994**|\n\n2. Impact of L_palette: The results from three new scenes confirm that including our L_palette loss consistently and significantly improves reconstruction quality (PSNR/SSIM) and prevents color deviation across all dataset types:\n\n**Tab 4**: Impact of L_palette (PSNR↑ SSIM↑)\n|Scene|w/o L|w/ L|\n|-|-|-|\n|Flower|25.57 0.933|**33.52 0.966**|\n|Lego|28.95 0.940|**31.72 0.958**|\n|Kitchen|30.30 0.888|**31.63 0.952**|\n\n3. Extended Time Comparison: We provide runtimes for three scenes from different datasets, demonstrating the stability and generalizability of our method's efficiency:\n\n**Tab 5**: Processing Times\n|Dataset|Time|\n|-|-|\n|LLFF|9.93min|\n|Blender|9.97min|\n|Mip360|10.14min|\n|Mean|10.01min|\n\n4. Impact of Residual Adaptive Gating KAN: We performed new ablations on three scenes for both segmentation and recoloring. The results of Tab 6 and 7 show our full design is consistently optimal. While its segmentation score is similar to w/o Res, it is significantly better than other variants, and as shown in Supp. Fig. 7, it converges much faster.\n\n**Tab 6**: Impact of structure (IoU↑ Acc↑)\n|Scene|w/o All|w/ Gate|w/o Res|w/ All|\n|-|-|-|-|-|\n|Leaves|0.144 0.948|0.000 0.939|0.969 0.998|**0.970 0.998**|\n|Bonsai|0.090 0.966|0.015 0.963|0.868 0.994|**0.868 0.994**|\n|Lego|0.037 0.973|0.000 0.972|0.888 0.997|**0.891 0.997**|\n\n**Tab 7**: Impact of structure (PSNR↑ SSIM↑)\n|Scene|w/o All|w/ Gate|w/o Res|w/ All|\n|-|-|-|-|-|\n|Flower|32.77 0.961|33.24 0.964|33.50 0.966|**33.52 0.966**|\n|Kitchen|30.81 0.931|30.79 0.937|31.02 0.948|**31.63 0.952**|\n|Hotdog|37.54 0.960|38.30 0.980|38.68 0.984|**38.95 0.988**|\n\n**Q7-**: Mip360 evidence?\n\n**Response**: We apologize for the oversight in our initial submission's video presentation. Our method's superior multi-view consistency is already demonstrated quantitatively in Table 3 of the supplementary material, and we have now rendered a full 360° video for the Mip-360 dataset as further visual proof, which we are unable to upload here.\n\n**Q8**: Ethical considerations?\n\n**Response**: First, we use only public, non-sensitive benchmark datasets. Second, our supplementary material (Sec. 10) discusses both the positive societal impacts and the potential for misuse."}]

Author rebuttal:

Meta-review: The paper introduces KaRF, a framework for local color editing of NeRFs using Kolmogorov-Arnold Networks (KANs). Key contributions include a residual adaptive gating KAN with GRBF activations and a palette-based loss, enabling improved boundary precision and reduced color bleeding. Reviewers highlighted the novelty of applying KANs to NeRF editing, the clear exposition, and strong results across benchmarks. Main concerns focused on the supervision level (multiple SAM masks, stronger than “weak”), limited ablations and direct MLP comparisons, slow runtimes (~10 min vs seconds for recent methods), and insufficient qualitative evaluation under complex lighting or 360° settings.

The rebuttal included additional ablations and clarified quality–speed trade-offs, addressing some architectural and efficiency concerns. However, questions remain about scalability, supervision strength, and evaluation breadth. Overall, I find the paper technically solid and innovative, with contributions of interest to the community. I recommend acceptance, as the strengths outweigh the weaknesses but stronger empirical validation would improve confidence in the claims. The Camera Ready version should include additional results and clarifications provided in the rebuttal.
Decision: Accept (poster)