NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
Authors: Andrea Dunn Beltran
Daniel Rho
Marc Niethammer
Roni Sengupta
Venue: NeurIPS 2025 poster ()
Keywords: SLAM
Endoscopy
Near-Field lighting
Primary area: machine_learning_for_sciences
TL;DR: NFL-BA enhances tracking and mapping in neural rendering-based SLAM by explicitly modeling dynamic near-field lighting, with applications in endoscopy.

Abstract: Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models near-field lighting as a part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SLAM can enable autonomous navigation, guidance to unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking, 37% for MonoGS and 14% for EndoGSLAM, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement in SLAM performance due to NFL-BA.

NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
Andrea Dunn Beltran "I Daniel Rho"!, Mare Niethammer', Roni Sengupta'
University of North Carolina at Chapel Hill University of California San Diego
Equual contribution
fasdunnbe, dn103c1, ronisen]@cs. unc.e.uunceee. edd, mmiethammer@uccd edu
with Dynamic Lightin
2D Captures
3D Reconstruction
Pose Estimation
Render D
SLAM Photo-BA
Render
SLAM with NFL-BA
Figure 1: NFL-BA enhances tracking and mapping in neural rendering- based SLAM (e.g..
MonoGS 27 by explicitly modeling dynamic near-field lighting, with applications in endoscopy.
Abstract
Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, sub- terranean robotics, and search & rescue in collapsed environments, require agents to operate with co-located light and camera in the absence of external lighting. such cases, dynamic near-fieh near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models wear-field lighting part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA be integrated into neural sadering-baseee SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SL.AM enable autonomous navi- gation, guidance to unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss SLAM systems with NFL-BA leads significant improvement camera track- ing, 37% for MonoGS and 14% for EndoGS, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement SLAM performance due to NFL-BA.
Introduction
Simultaneous Localization and Mapping (SLAM) enables autonomous agents to build spatial map of an unknown environment while estimating their own poses within it, with wide-ranging applications robotics, computer vision, autonomous vehicles, and scientific imaging. Most SLAM systems
49 20 27 [6] assume autonomous agent navigating environment with distant, static illumination, e.g., a self-driving car in the streets, and they optimize a Photometric Bundle Adjustment loss where they minimize error between the captured image and the re-rendered image using estimated 3D scene and camera poses.
38
34 7
58
60
56
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Photo-BA
GT
Photo-Ba BA
NFL-BA
Static Lighting
Dynamic Lighting
Photo
Figure 2: MonoGS performance under (1) distant static lighting and (2) dynamic near-field lighting from a co-located flashlight. Standard photometric BA performs well under static lighting but fails under dynamic lighting, degrading both trajectory and map quality. NFL-BA restores performance under dynamic lighting, matching the quality of the static-light setup.
However, many scientific and safety-critical applications demand that autonomous agents operate in environments devoid of external illumination, relying instead on self-mounted light sources. For example, in endoscopy procedures, a slender flexible tube with a co-located light and camera used to inspect internal organs such as the airway and the colon 9 45 33
51 17 Accurate trajectory estimation is crucial for reliably guiding instruments to areas of interest, mapping anomalies, and collapsed- -biidding
23
avoiding tissue damage during navigation. In subterranean search-and-resse inspection, robots rely on onboard lamps to explore unstable voids; slight errors in pose estimation can accumulate into large drift, leading to misaligned maps, missed victims, or costly back-tracking.
Despite the prevalence of these use cases, current SLAM systems perform poorly under such conditions (see Fig. 2 This performance drop is primarily due to the effects of dynamic near-field lighting. where the only illumination is co-located with the camera and moves with Dynamic near-field lighting causes different points of the surface to receive different intensities of light at each time step, depending on the distance and orientation of the point to the camera, introducing strong, view-dependent shading. These lighting artifacts significantly impair both feature and direct (photometric) tracking, resulting in substantial failures in mapping accuracy and pose estimation.
To alleviate these issues, we propose a new Bundle Adjustment loss that accounts for dynamic near-field lighting. Our key intuition is that the shading effect of the captured image can provide valuable information about the relative distance and orientation between the surface and the camera. With this, we formulate a Near-Fiell Lighting Bundle Adjustment loss, NFL-BA, where we optimize the surface geometry and the camera parameters such that the rendered image has shading variations that match the relative distance and orientation between the surface and the camera. Our NFL.-BA loss can be applied to any neural rendering-based SLAM algorithm, i.e., with neural implicit and explicit 3D Gaussian scene representation.
In this paper. we specifically focus on demonstrating how NFL-BA can improve the performance of existing SLAM systems for 3D reconstruction and localization from endoscopy videos. SLAM can enable autonomous navigation through internal organs and guide physicians to unsurveyed regions to improve physicians' situational awareness by providing 3D visualizations, and can help measure organ shapes. We evaluated NFL-BA with two state-of-the the-art
SLAMM systems, general-purpose MonoGS (27 and endoscopy-specifie EndoGSLAM 4S . and one neural implicit SLAM, NICE-SLAM (58], by replacing their Photometric Bundle Adjustment loss with NFL.BBA loss. We observe that the NFL-BA loss improves the performance of all SLAM algorithms on average when using ground-truth or estimated depth maps on the C3VD colonoscopy dataset. For example, NFL-BA significantly improves MonoGS by reducing camera tracking error by 37% (3.48 to 2.18 mm) and camera mapping error by 38% (1.59 to 0.99 mm) when initialized by PPSNet depth (35)
Additionally, also demonstrate the effectiveness of NFL-BA on indoor rooms captured with moving co-located light and camera without any external light source, mimicking agent navigation during search & rescue and covert military operations. By replacing incorporating our NFL-BA loss, we see an average improvement of ~35% in pose estimation across all scenes.
2 Related Works
Dense SLAM and Bundle Adjustment. Early SLAM pipelines focused on sparse feature matching
for pose estimation and mapping [30 6 42 [.. With advancements in neural scene representations

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several proposed SLAM frameworks [58 2 generate dense, pixel-level that yield more detailed and
robust reconstructions. More recently, 3D Gaussian surface methods have demonstrated real- time
rendering with high-fidelity mapping 20 27
49
10
53
These dense SLAM approaches all rely on core Bundle Adjustment step. Bundle Adjustment (BA) alternatively optimizes camera parameters and surface geometry by minimizing errors across multiple frames. Traditional geometric BA aligns detected 2D feature points their 3D counterparts by minimizing reprojection error, assuming static lighting and Lambertian surfaces [4]. Although effective controlled environments, it struggles in complex or low-texture scenes. Photometric BA (Photo-BA) BB) [ incorporates pixel intensities into the optimization process, minimizing photometric re-projection errors and proving advantageous in environments where feature matching fails ([T). However, Photo-BA does not exploit the correspondence cues provided by dynamic or near-field lighting where image intensities vary across frames.
Near-field Lighting models. Near-field lighting has been leveraged for 3D reconstruction tasks like monocular depth and surface normal estimation [35]57] and Photometric Sterco [21]. Some of these approaches [35|21] use a near-field lighting representation as input to a CNN along with captured images for predicting surface normal and geometry. In the context of Endoscopy, LightDepth [37 and PPSNet 335 demonstrated the effectiveness of near-field lighting to enhance depth estimation. LightNeus exploited the inverse inverse-square law for light decay to improve endoscopic surface reconstruction, however with known camera parameters and pre-operative 3D CT scan.
It has never, however, been used for Simultaneous Localization & Mapping (SLAM) problems, let alone in combination with neural rendering methods. To this end, we propose a Bundle Adjustment Loss with Near-Field Lighting (NFL-BA), considering the most commonly available single co-located camera & light in the endoscope or other autonomous agents.
Dynamic Lighting in SLAM. Visual SLAM performance often degrades under illumination changes such as exposure shifts, specularities, and varying color temperature. Early photometric calibration methods jointly optimize camera intrinsies, exposure, and scene depths normalize brightness variations in real time [T] while probabilistic SLAMs with unscented filtering further stabilizes pose estimates under uncertain lighting conditions (26). More recently, learning- based matchers [2]48] 48) adapt descriptors to cope with complex lighting variations. None of these methods, however, explicitly model near-field lighting geometry handle this co-located light setting.
SLAM in endoscopy. Early works 400, 12 demonstrated the feasibility of applying SLAM in such enviromments by addressing dynamic lighting and tissue deformation. Researchers have often used a mixture of supervised learning on synthetic and self-supervised learning on real endoscopy datasets for tailoring SLAM frameworks to endoscopy with complex camera motion 25 [55] 46 and
developed novel endoscopy SLAM frameworks [3] 29 18). However these techniques often struggle
with challenging sequences from both synthetic and clinical data. Recently, neural rendering based
methods [39 23 45 13 [5] have proved especially effective in generating high-quality details
and modeling textureless regions with large number of Gaussians. In this work, adopt neural rendering approaches and explicitly model the near-field lighting effects, alleviating dynamic lighting challenges and improving performance.
3 Background
In this section, we review the general framework of neural sandering-based SLAM. We represent the camera at time t by its extrinsics Pt- [R, Til SE((3) and known intrinsics K, yielding the projection क. = K P,. We assume the camera intrinsic K to be the same for all frames and known or calibrated ahead of time. Pixels are denoted and 3D camera-space points by
In neural rendering, scene parameters O whether in the form of neural networks or primitives, encode visual and geometric information, such as colors c; and occupancy a, Given è and P., we can get the color C(-) and the depth D-)) of pixel p from frame at time t as follows 28 20):
C(p) = Σ (G -1))),, D(p) = Σ 2, (1-0g) az)
(1)
iEN
VEN
where N' denotes the group of samples for a pixel p. with a; representing the occupancy of the i-th sample, and z, denotes its distance from the camera center.
To optimize P. and O dense SLAM methods typically use rendering loss Lren reducing the rendering errors between the rendered and captured images [58 27 49 and, if estimated or ground
3

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truth depth maps are available, an additional depth loss geo can be added 443. Typically, these losses take the form of L" norm as follows with variations with M, as a pixel mask:
Pren
M の (C llbps
Lyea = M @ (D D)Ip
(2)
Bundle adjustment optimizes both P. and @ using the following combined loss:
Photo-BA:
min Σ (e,nCrenCC,,taae + Dg M.)
(3)
tEW
where W denotes the set of frames used for the bundle adjustment and the hyperparameters Xyen and Agea are the loss weights. Additionally, the objective function can include any other regularization terms, such as artifact suppressing [27 or opacity regularization [59]
During the Mapping stage, both O and P: are optimized over of keyframes. The exact algorithm for keyframe selection, keyframe update and optimization strategies for tracking and mapping phase vary between different SLAM approaches and their specific objectives.
Implicit Neural Representations. Neural field-based SLAM methods
60 38
uses a set
of neural networks F(x, d: )) + (c,, o,), optimized to estimate the color c, and the volume density og for an input 3D coordinate x and the view direction d. The the occupancy can be calculated from the volume density σ; and the distance between adjacent samples 6; as a; 1
3D Gaussian Splatting. For 3D Gaussian Splatting (0 SL.AM methods, the scene is represented by set of Gaussians with mean μú', covariance E' in world space, color ct, and opacity a'. The shape parameters and occupancy a² of the splatted 2D Gaussians are computed as follows:
1 = Tepu: E = J. a; = o² exp( -xp(-5 n1) (
(p (p- x-)
(4)
where Jj the Jacobian of the projection T., p denotes a pixel coordinate, and pú, Eg are the splatted mean and covariance of Gaussian G in pixel space.
4 Near-Field Light Bundle Adjustment
We introduce novel Fiel-Field Lighting based Bundle Adjustment loss, NFL-BA, that inte- grates near-field lighting with neural-rendering 3D scene representations to improve perfor- mance of existing SLAM systems on images captured with dynamic lighting co-located with the camera. Our proposed NFL-BA can replace commonly used Photometric Bundle adjustment loss, defined in Eq. 3 within neural-rendering based SLAM framework. Photo-BA typically optimizes scene appearance parameter as RGB color, which is suffient when the illumination on each scene point remains the constant throughout the capture. However, for scenes with a dynamic light co-located with a moving camera, the illumination received at each point varies per frame the camera and the light moves through the scene. In this setting, illumination received at each point depends on the relative distance and orientation between the point and the camera, as conceptualized in Fig. 3 Thus continuing to model scene appearance as simple RGB color inaccurate for dynamic near-fiell lighting it doesn't It separate effects of illumination due to camera movement from the intrinsic view-inde wew-independent color the scene, i.c. albedo.
Co-located light and camera
Incoming Rediance Surface Normals
Frame
Frams
Figure 3: Illustration of our key idea. As the co- located light and camera, moves through the scene, different 3D Gaussians on the surface receive dif- ferent intensities of light (red arrow), dependent on the relative distance and orientation between the 3D Gaussian and the camera.
Our goal is to explicitly model surface appearance albedo and separate pear-field lighting effects from it. To accurately model dynamic lighting we then represent near-field illumination effects with camera pose and scene geometry. In sec. 4. we describe our image formation model using neural rendering framework that will decompose the surface appearance into albedo and incoming lighting, which will be further represented as function of scene geometry and camera pose. Then in sec. 4.2 we will use this image formation to create the Near-Fiehl Field Bundle Adjustment loss and show how can be casily integrated into neural rendering based SLAM framework.

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Ground Truth
Reconstructed
SI-MSE
Albedo
Normals
PPS
RGB Images
Error
Features from input Image and Depth
Figure 4: Image Formation Validation. We show that C3VD images captured with real endoscope conform our co-located light-camera zero attenuation & image formation model, as indicated by very low per-pixel scale-invariant MSE between the original image and the reconstructed image with masked-out specular regions.
4.1
Image Formation with Near-Field Lighting
We consider an maage-formation model under near-field lighting for single image following previous works [99 35 Each pixel p and the corresponding tiree-dimensiona point Tp in the camera space receives different light intensities and directions, characterized by the light source surface direction (.) and attenuation term I. (.), as follows:
(L))x))
1 (xrp) = Tp JT.
(. = 1 x2
(5)
Ip xzll
where Fy, is the location of the light source, f is the forward (optical axis) vector, 3 is an angular attenuation coefficient, and will be discussed in sec. 4.2
Assuming diffuse reflectance model, which has proven effective for depth estimation in endoscopic
scenes 35
approximate the rendered image at each pixel Ō(-) as:
PPS(#,)) (xp))) (xr)) TT(x)))
C(p) PPS))))))))))
(6)
where p(-) and n².) are albedo and normal at position Tp of pixel respectively. PPS(.) is a per-pixel shading term. Note that existing approaches that uses this near-field light image formation model [9]35] uses pixel-based representation to predict depth map or surface geometry from images captured from single viewpoint only. In this paper, we extend the Near-Field Image Formation model beyond single-view pixel- based representation to multi-view 3D representation.
Our key insight is that the standard volumetric rendering equation can be modified to incorporate the near-field lighting model described in eq. 6 while keeping the overall SLAM pipeline intact. In our framework, we reinterpret the direct color (c; in eq.3) as the product of the albedo p(-) and the shading term PP)(.), which models dynamic near-field lighting. Note that both albedo and shading is defined directly on the 3D neural representations, i.e. neural radiance field or 3D gaussians, and not in pixcl-space. This leads to the modified rendering equation under near- field lighting:
Cpps(0 (p) = > P(sx))PP((
iEN
Note that eq. represents a special case of eq. 7 where a single sample is considered and the occupancy a; equals one. Our image formation model assumes diffuse reflectance and no angular attenutation, to reduce the complexity of the modeling. While it is easy to extend the image formation model to handle specular reflectance and angular attenutation of lighting, this leads to additional parameters that needs to be optimized during the Bundle Adjustment.
Angular attenuation. Following previous works ?? 35 we simplify the near-field light image formation model by setting the attenuation coefficient 3 in eq. to zero. This effectively ignores the directional fall-off component, reducing the light attenuation term to simple inverse-square fall-off (xxx,) = 1/ æy 122 This simplification is justified because the angular attenuation settings like endoscopy is often negligible compared to the inverse square law attenuation, and estimating 3 accurately can be challenging due to variations in endoscope designs. In future work, we plan determine the optimal value of / for different systems and incorporate the light direction vector rf for more accurate modeling which can further improve camera rotation during Bundle Adjustment.
Empirical validation of our image-formation model on colonoscopy image. Fig. 4lg provides an example colonoscopy image from the C3VD dataset 4, showing the accuracy of the near- field model (Eq. () with 3 of 0. Albedo was estimated by converting each RGB image to HSV color
5

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space, setting the value channel to 1 across all pixels, then converting the modified image back to RGB space. This standardizes pixel intensity variations, approximating a reflectance map where illumination effects are minimized, but does not strictly represent ground truth albedo. As shown, the image formulation model is sufficient to represent endoscopic scenes with low reconstruction errors.
4.2 Near-Field Light Bundle Adjustment Loss
Next, we will re-define the Photometric Bundle Adjustment loss of eq. 3 Jusnng the near-field lighting based image formation model defined in eq expresseed as follows:
NFL-BA:
min
Arenfren (Cpp: C:Mi) + Ageoogeo D; M,)
(8)
tEW
where Ćp Cyps denotes the rendered image with wear-field lighting iiniingoooooorreddd volumetric rendering equation (Eq. [) This reformulation seamlessly integrates near-field lighting cues into the neural rendering framework without altering the rest of the SLAM framework. Since formulation is confined solely to the rendering process, and thus to the bundle adjustment, we do not modify or replace any other SLAM components for fair comparison. This design choice enables easier integration with existing neural rendering-based SLAM methods.
Choice of image space in optimization. Note that many settings, and especially endoscopy, frames are stored in standard sRGB color space, whereas our near-field shading term PPS(.) is computed in apply inverse gamma correction of 2.2 to the sRGB
linear space. To ensure consistency, images before computing PPS. or equivalently, gamma-correct the linear PPS output by 7 = 1/2.2 when rendering back sRGB. This step aligns the lighting model with the true photometric intensities and prevents bias from the nonlinear sRGB transfer function.
Normal calculation during Bundle Adjustment. To calculate the normals n²-) from neural fields, we utilize the direction of the gradient of the occupancy with respect to the spatial coordinates as follows [5] n(x;,) -V (xx()))/ (a;x:)] For Gaussian Splatting, we use the shortest axis of each Gaussian as its normal, following [52 47). In both cases, we ensure the computed normal is oriented towards the camera by enforcing n [x)) to be positive. Otherwise, we flip the normals
by multiplying them by for stability.
5 Evaluation
Table 1: Quantitative Evaluation on the C3VD
Our proposed method is a plug-in approach that can be applied to any existing neural- rendering-based SLAM framework. We first test our method on endoscopy videos using one neural implicit SLAM, NICE-SLAM [58).
dataset with oracle depth map. Replacing
Photometric BA with NFL-BA significantly im-
proves tracking quality of two state-of-the 3D
Gaussian SLAMs, MonoGS (77 and EndoGS [59
and one neural implicit SLAM, NICE-SLAM [8
as well as two existing 3DGS-SLAM frame- works: the general-purposs purpose MonoGS 27 and the endoscopy-specti
Tracking
Mapping
EndoGSLAM
45 In
Method
ATE, (mm))
ATE, (P
Chamfer (mm))
each case, we replace the standard Photometric Bundle Adjustment loss (3) with our proposed equation NFL-BA loss g Additionally, we
NICE-SIAAM,
Photo NEL
4.16
2,68
1.95
CVPR 22
2,88
1.70
EndoCSLAM. MICCAI'24
Photo
1.93
1.81
0.85
also test MonoGS [27] on self-captured indoor scenes with co-located light and camera.
NFI.
2.04
1.13
0.97
MonaGS,
Photo
2.90
1.11
1.16
CVPR'24 24
NFL
1.60
1.49
0.79
5.1 Evaluation Setting
Datasets. We evaluate our method on three datasets that reflect different challenges in handling near-field dynamic lighting: (1) a phantom endoscopy dataset, (2) a clinical endoscopy dataset, and (3) dataset of indoor scenes captured with phone camera with flashlight turned on.
C3VD. The C3VD dataset 4 (CC BY-NC-SA 4.0) was created using a phantom colon with syn- thetic materials to simulate realistic tissue geometry. Colon /OK. To test generalization in real- world clinical endoscopy settings, we evaluate on Colon 10K [24). a large-scale video dataset without depth pose supervision. Videos are sampled from actual The endoscopy video was captured by a surgeon who performs different endoscopy procedures on the phantom colon with real endoscope capturing RGB images coupled with corresponding depth maps. We focus on 8 sequences ranging from 70 to 800 frames from different regions of the colon, for more details, please see supplementary. We evalute using both ground truth and predicted depths.

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Table Quantitative evaluation on the C3VD dataset using depth maps estimated by SOTA Replacing Photometric BA with NFL-BA
techniques, PPSNet [35] and DA-Hybrid [50 32
significantly improves tracking for both MonoGS 7 and EndoGSLAM (45, and mapping and
rendering quality for MonoGS [27]. Note that SOTA performance for each of the tracking, mapping,
and rendering metrics is observed when NFL-BA is used.
Tracking
Mapping
Rendering
Method
Depth
BA
ATE, (mm)
ATE; +
Chamfer (mm)
LPIPS
PPS-Net
Photo
3.03
1.73
1.23
0.39
EndoGSLAM 45
PPS-Net
NFL
2.62
1.24
1.25
0.39
2ICCA/224
DA-Hybrid
Photo
6.67
2.26
2.12
0.43
DA-Hybrid
NFL.
3.91
1.58
2.39
0.42
PPS-Net
Photo
3.48
1.70
1.59
0.56
MonoGS 27
PPS-Net
NFL
2.18
1.65
0.99
0.53
CVPR'24
DA-Hybrid
Photo
4.63
1.69
1.34
0.52
DA-Hybrid
NFL
2.35
1.14
1.13
0.52
procedures and are typically around 300-600 frames. This setting is signifi- cantly more challenging than the phan- tom setting, since frames may contain motion blur, specular highlights, and fluid occlusions. Sequences are uni- formly sampled and fisheye corrected,
tanns
sigmold
wts 2.4
2.17
Onnce
Self- Capture Indoor Scenes. To study the role of near-field lighting in controlled non-clinical setting, we capture a dataset of four indoor scenes (Guitars, Porch, Pool, and Stairs) using an iPhone 15 Pro. Scenes include objects with varied geome- try and reflectance (diffuse, specu- lar), imaged under dynamic motion. Scenes are captured using a co-located point light source mounted cam- era. Ground-truth camera trajectories were recorded via motion capture, no reference point clouds are avail- able; hence, we report only trajectory error (ATE,) and perceptual quality (LPIPS), omitting Chamfer distance. Details and additional visualizations are included in the supplementary.
wr. 5.00
DA-Hlyrid
Figure 5: Camera tracking improvement over En- doGSLAM [45]. Replacing the Photo-BA loss (in blue) with NFL-BA loss (in red) significantly improves camera tracking for different depth initialization. Average tracking error ATE, for each sequence is reported in the inset. (zoom for details)
Metrics. For evaluation, we basically followed other neural rendering SLAM algorithms 22 45 For
tracking performance, we measure the root mean square error the Absolute Trajectory Error (ATE) for both translation and rotation across all frames. Translation error ATE, is in millimeters (mm) for the endoscopy scenes and meters (m) for the in-door scenes. And rotation error ATE, is in degrees. To assess the mapping quality, we use the Chamfer distance from ground truth point clouds to the nearest points the estimated point clouds [46), for more details please see supplementary. In addition, we evaluate rendering quality using the I Learned Perceptual Image Patch Similarity (LPIPS) (54). We note that for many endoscopic SLAM applications, tracking and mapping accuracies are more important than photorealism of the rendered images, unlike many indoor or outdoor scenes.
Computational costs We trained all models on single NVIDIA RTX A6000 GPU. The per-scene optimization takes ~1 FPS. For more information on runtime speed, please see supplementary materials.

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desc
sigmoid
Proto 10:30
Photo 0.25
Phote 2
1.51
Oracle
PPSNer
61
61
Photo 10
DA-Hybrid
Figure Camera tracking improvement over MonoGS [27]. Replacing the Photo BA loss (in blue) with NFL-BA loss (in red) significantly improves camera tracking for different depth initialization. Average tracking error ATE, for each sequence is reported the inset.
Oracle
PPSNet
DPT-Hybrid
cecum t1
Photo-BA
1.16
mm22m
1.64
NFL-BA
Ground Truth
0.95 mm
1.04 mm
1.64 mm
Oracle
PPSNet
DPT-Hybrid
Photo-BA
trans t2_
NFL-BA
Ground Truth
0.97
1.09
1.44 mm
Figure 7: Reconstructed point clouds using MonoGS [27] show that NFL-BA improves coverage
and density while reducing scatter compared to Photometric BA, measured by Chamfer distance.

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MonoGS Photo- -B
MonoGS NFL-BA
EndoGSLAM
Soq.
Soq.
Figure 8: Results real endoscopy from Colon10k dataset. On Sequences and 4 with PPSNet depth, NFL-BA improves MonoGS tracking and mapping, yielding more coherent, elongated colon structures, while EndoGSLAM fails under sudden camera motion.
5.2 Evaluation on C3VD Endoscopy Data
Because NFL-BA designed as drop-in replacement for photometric bundle adjustment, we only adjusted the two associated loss weights; all other hyperparameters remain identical between the Photo-BA and NFL-BA experiments. Please see supplemental for detailed hyperparameter settings.
SLAM with oracle depth map. In Tab. [ we replace Photometric Bundle Adjustment loss with NFL-BA loss for depth is initialized with ground or oracle. NFL-BA significantly improves camera localization (ATE, and mapping for VICE-SLAM and MonoGS, and only camera rotation E,) for EndoGSLAM. EndoGSLAM was specifically designed for synthetic data with an oracle depth map, and we will show later that for estimated depth maps or real endoscopy videos, it performs significantly worse than MonoGS Ground-truth depths are never available during endoscopy, and the majority of endoscopes hardly have any depth sensors.
SLAM with predicted depth map. Under realistic conditions with estimated depths, NFL-BA's impact is even more pronounced. In Tab. 2 we replace Photometric BA loss with NFL-BA loss for MonoGS 277 and EndoGSLAM 45 for depth maps we PPSNet (35) state-of-the-an monocular depth estimation algorithm for endoscopy, and fine-tume turned general
ceneeall-uupper purpose depth estimator, which
we will call it as DA-Hybrid DepthAnyyhhngg555 (50 with DINOv2 encoder (32). NFL-BA significantly improves camera localization (AT' E,) and camera rotation (ATE, for both MonoGS (7) and
EndoGSLAM 45 while producing similar rendering quality. For example, camera localization for MonoGS is improved by 37% for PPSNet and 49% for DA DA-Hybrid depth initialization. Mapping accuracy of MonoGS also improves by 37% for PPSNet and 16% for DA-Hybrid depth maps. Overall, these results demonstrate that NFL-BA can compensate for noisy depth estimation and improves performance, Across all four metries, for tracking, mapping, and rendering, the SOTA performance on the C3VD dataset is in fact achieved when NFL-BA loss is used in the SLAM framework.
5.3 Evaluation on Real Endoscopy Data
We show results on real endoscopy sequence from Colon10k sequence 3 and in Fig. En- doGSLAM fails to construct any real structure, with many disconnected regions along spiral trajectory. EndoGSLAM assumes constant velocity and is not robust to the sudden motion common in endoscopy procedures, which is significantly more in real data than C3VD. This results extremely poor or failed reconstructions.
Sequence 4. This pull-back Nlown-the-barred sequence exposes a clear cylindrical lumen. With Photo-BA, MonoGS captures the overall shape but produces broken segment due to trajectory drift. NFL-BA corrects this, yielding continuous "hollow-center" reconstruction. Minor artifacts from extreme specular highlights remain (green points), as detailed in the supplement.
In the extended traversal, both Photo-BA and NFL-BA recover the colon's 's general geometry, but NFL-BA produces a longer. tighter model with less point scatter. It also better preserves interior ridges (interactive point clouds in the supplement).
Sequence 3.
5.4 Evaluation on Indoor Scene
To validate NFL-BA in a non-medical setting, we evaluate on four indoor scenes. Table shows that replacing standard Photometric BA with NFL-BA yields substantial reductions in ATE, across

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Photo-BA
NFL-BA
GT
Photo-BA
NEL-BA
0.33
Guitars
Photo-BA BA
NFL-BA
GT
Photo-BA
NFL-BA
635
Porch
Figure 9: Results on indoor scenes captured with co-located flashlight and phone camera. Qualitative comparison on two self-captured indoor scenes using MonoGS with standard Photo-BA versus NFL-BB. (left) Estimated camera trajectories overlaid on ground truth. (center) Per-frame tracking error relative to ground truth. (right) Example re-rendered views, illustrating the sharper, more accurate reconstructions enabled by NFL-BA.
Table 3: Quantitative results on four self-captured indoor scenes under dynamic lighting, comparing MonoGS with standard Photo-BA versus NFL-BA. For each scene, the best of each metrie is bold.
Guitars
Porch
Pool
Stairs
BA
ATE, (m)4
LPIPS 4
ATE, (m)4
LPIPS +
ATE, (m)
LPIPS +
ATE, (m))
LPIPS +
Photo
0.30
0.39
0.50
0.49
0.41
0.46
0.36
0.40
NFL
0.18
0.37
0.35
0.50
0.30
0.44
0.20
0.31
all scenes: from 0.30m to 0.18m (40%) in Guitar, 0.50m to 0.35m (30%) in Outdoor, 0.41m to 0.30m (27%) in Pool, and 0.36m 0.20m (44%) in Stair. On average, NFI.-BA reduces tracking error by ~ 33%, demonstrating that near-field shading cues greatly enhance pose estimation even in richly textured, well-lit indoor environments. While LPIPS remains largely comparable, with slight improvements in Guitars and Stairs and minor variations in Porch and Pool, the primary benefit of NFL-BA is clear in trajectory accuracy (see Fig.
Conclusions
In this paper, we presented novel bundle adjustment loss that explicitly models dynamic near- field lighting by incorporating light intensity fall-off based on the relative distance and orientation between the surface and the co-located light and camera. This formulation especially effective for endoscopic scenes, where traditional geometric or photometric bundle adjustment losses struggle under dynamic near-field lighting conditions on textureless surfaces. We demonstrated the general applicability of our approach by integrating it into three different neural rendering- based SLAMM methods, improving performance on a challenging endoscopy dataset and indoor scenes captured with phone camera with a flashlight turned
Limitations. While our new formulation for SLAM effectively represents scenes with co-located and dynamic lighting environments, it is currently limited in handling specular reflections, sub-surface scattering, and inter-reffections. Incorporating more complex image formulation beyond the scope of the current work, and addressing these remains a promising direction for future research.
Acknowledgments
This work is supported by a National Institute Health (NIH) project##222BB2B #R21EB035832 Modeling of Endoscopy Videos" and #R21 "Gen-Al Airway Simulator for 3D Endoscopy". We also thank Stephen M. Pizer, Ron Alterovitz, and Dr. Sarah McGill for helpful discussions during the project.
"Next-gen
10

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Supplementary Materials
Overview of Appendices
Our appendices contain the following additional details:
Appendix
Dataset Processing and Collection
Appendix
Implementation Details and Computational Costs
Appendix
Point Clouds and Metrics
Appendix
Long Sequence Validation
Appendix
Evaluation of Center-Crop Baseline
Appendix
Lighting model and Angular Attenuation
Appendix
Results on Endoscopy Datasets
A Dataset Processing and Collection
C3VD. We test our method on subset of videos with at least one video from each section of the colon, with varying camera motion, and anomalies. We choose these 8 videos from the test split of PPSNet to avoid any bias when the SLAM is initialized with PPSNet predicted depth map. The names of the sequences are as follows: cecum tl a, cecum 12 a, cecum 13 a, sigmoid (3 desc_ [4 __p2, trans trans_t2_a a, trans_tš a, and trans 14
All images were cropped to remove any artifacts resulting from fish-eye correction then downscale and crop the images. Specifically, we resize each image to have height of 384 pixels while maintaining the aspect ratio, then crop the central region to obtain 384x384 pixel image.
Colon10k. Since Colon10K provides no ground-truth depths, compute per-frame estimates with PPSNet. Each image is center-cropped and uniformly resized to 384 x 384 px to match our SLAAM input requirements.
Indoor Self Captures. We recorded four indoor scenes using an iPhone 15 with LiDAR, capturing synchronized RGB (1440 1920 px) and depth (256 192 px) streams. Raw RGB frames downscaled to 256 x 192 px to align with the depth map resolution. Depth maps are stored as 16-bit values up to 10 m. We logged camera poses via Apple's ARKit framework, code for our custom capture app and preprocessing scripts will be released alongside the dataset.
B Implementation Details and Computational Costs
As mentioned in the main paper, per-scene optimization takes approximately 1 FPS. We found that NFL-BA only reduces the fps runtime by small amount the C3VD dataset.
all models single NVIDIA RTX A6000 GPU. The
NICE-SLAM. Since the scene is encoded using neural networks, we extract normals from the occupancy grid, as described in Sec. 4.2 to calculate the shading term. NICE-SLAM requires well-defined bounding box which we obtained from the ground truth point clouds (see appendix C). We also used the default loss weights of NICE-SLAM, setting Aren to 0.5 during tracking and 0.2 during mapping, and Agea to in both phases.
The main difference is the weight map Mj in the bundle adjustment loss (Eq. 3) to exclude over-exposed pixels that can arise endoscopy-speciff lighting conditions. Furthermore, given that the shading term PPS(-) is sensitive to depth scales, we rescaled the depth maps so that their maximum values are approximately 5. Notably, scaling the depth maps did not improve baseline performance (when using PPS depth maps, the average AT E, went from 3,03 to 3.38). We used the
EndoGSLAM.
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Table 4: Runtime: We evaluate the frames per second (FPS) for all methods on the C3VD dataset.
Method
Depth
Photo-BA
NFL-BA
Oracle
<>
< I
NICE-SLAM
PPSNet
<1
<1
Oracle
1.79
1.35
EndoGSLAM
PPSNet
1.53
1.22
DPT-Hybrid
1.20
0.90
Oracle
1.38
1.09
MonoGS
PPSNet 1.06
0.93
DPT-Hybrid 0.99
0.83
default loss weights of EndoGSLAM, Areni Apeo set to 0.5 and during tracking and and during mapping, respectively.
MonoGS. To integrate our method, we treat the Gaussian color features albedo features and multiply them with the shading term before rasterization, and then we use the rendered output colors for bundle adjustment. For all input depths. we Aren and Ageo 0.8 and 0.5, respectively.
C Point Clouds and Metries
Chamfer Distances. Since ground truth point clouds are unavailable for C3VD, we generate them by unprojecting 2D images into 3D space with the correct camera configuration and the oracle depth maps, provided in the C3VD dataset. For neural fields-based SLAM, we use the vertices of the output meshes as the estimated point clouds, while for Gaussian Splatting-based SLAMs, we use the Gaussian positions. For point cloud alignment, we use Coherent Point Drift [3T and the Chamfer distances are also in millimeters.
Coloring Point Clouds. We extracted point clouds from 3D Gaussian positions for visualization. For colors, we directly use Gaussian color features.
D Long Sequence Validation
To evaluate NFL-BA's performance on longer sequence, we test our method on a longer screening video for C3VD, specifically e0 fullll t2 v2, which spans over 4,000 frames with ground-truth poses, allowing us to measure cumulative drift. In the main paper, we only evaluate on registered videos with ground truth depth, not screening videos. We show how NFL-BA performs relative Photo-BA using the MonoGS backbone in Table
NFL-BA matches Photometric BA short sequences and increasingly outperforms it as trajectory length grows. As a plug-and bundle-adjustment loss, NFL-BA enhances long-term robustness under dynamic field lighting.
Table ATE_T (cm) on c0 c0ulullll__0eee000llllllll full_11_vi v2_v2 at varying lengths. NFL-BA matches or beats Photo-BA on short sequences and shows long-term term stability.
BA
500 frames 1,000 frames
2,000 frames
4,000 frames
Photo
2.599
1.937
8.459 5.742
14.790 11.368
NFL
2.422
2.254
E Evaluation of Center-Crop Baseline
We compared MonoGS Photo-BA optimized only the central 75% and 50% of each frame against full-frame MonoGS + NFL--A on two sequences, measuring translational ATE (ATEr), Chamfer distance (CD), and reconstructed point count in Table
16

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Although center- cropping improves camera tracking performance (ATE_ T) of MonoGS and gets close to NFL-BA, results in significantly worse reconstruction terms of quality (CD) and density (number of points). This is because for camera localization, not all pixels are essential, and focusing only on central pixels can eliminate near-field lighting effects. In contrast, for reconstruction, all pixels matter. This highlights the need for principled mechanism for handling dynamic near-field lighting, as proposed by NFL-BA.
Table 6: Center-cropping improves trajectory error but reduces map density, while full-frame NFL-BA maintains both accuracy and dense reconstructions.
Method
trans t3_a
desc 14 p2
ATET
CD
Points
ATET
CD
Points
MonoGS NFL-BA
0.26
0.60
28,196
0.13
0.67
8,879
MonoGS (full frame)
0.31
0.76
7,095
0.25
0.76
6,398
MonoGS (75% center)
0.35
1.06
5,761
0.13
0.97
4,329
MonoGS (50% center)
0.40
2.82
1,865
0.15
1.56
2,100
F Lighting Model and Angular Attenuation
We clarify that NFL.-BA models only diffuse reflectance and direct illumination, ignoring specular and subsurface effects. Explicitly modeling these introduces non-differentiable and highly non-convex terms, remaining open challenge.
To mitigate specular highlights, we apply an intensity mask discarding pixels above 0.9 grayscale intensity. This handles most artifacts but cannot correct reflective surfaces beyond the mask. While effective matte datasets (C3VD, in-the-wild), degradation occurs on Colon 10K with more specular highlights. Future work will address specular reflections under dynamic lighting.
Regarding angular attenuation, 3 is justified for tightly collimated endoscopic LEDs, must validated this for non endoscopy scenes. Using NFL-BA on the MonoGS backbone, we validate various values two indoor sequences seen in Table 7
provides competitive mean accuracy and low variance. Non-zero values may yield small gains but introduce instability. Given minimal benefit versus tuning cost, we retain = 0, and plan to explore learning 33 as a per-scene parameter future work.
Table 7: Ablation of the angular-attenuutioo coefficient 3 on two indoor sequences, reporting translational ATE and LPIPS.
Scene
Metric
[80.00
$=0.25
3=0.50
B=0.75
3=1.00
Guitars
ATE T
0.17:+0.01
0.17+00003
0.16=0.006
0.17+0.011 0.17:00006
LPIPS
0.36+00.02
0.3710.006
0.40:10.030
0.3610.017
0.36+00000
Porch
ATE T
0.33:0.116
0.25+:0013
0.27+0.045
0.22+0.012 0.3610.158
LPIPS
0.50+10.3
0.53+10.008
0.4810.005
0.5110.035
0.4910.000
G Results on Endoscopy Datasets
For all experiments for each of the slam systems, we report the median of three runs for all tables and figures. We have included per-sequence metrics for the median below. Additionally, include point clouds for EndoGSLAM in Figure 10 more can be found on our project page.
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Oracle
PPSNet
DA-Hybrid
Base
0.86
1.34
3.90
NFL
Ground Truth
0.80 mun
1.15 mm
5.08 mm
Base
0.32 mm
0.56 mm
1.02 mm
Ground Truth
NFL
0.26 mm
0.39 mm
1.49 mm
Figure 10: EndoGSLAM Point cloud results for 2 sequences: cecum tl a under review (top) and
desc 14 a Lal_a_2______ _2__underrrevier (bottom).
Table 8: Results for sequence cecum t1 a undder_review review
Method
Depth
BA
ATE, (mm)
ATE,
Chamfer (mm)
Photo
2.65
2.82
0.08
Oracle
NFL
1.39
2.81
0.15
NICE-SLAM
Photo
10.14
2.71
0.51
PPS-Net
NFL
4.14
2.75
0.19
Photo
1.39
0.24
0.12
Oracle
NFL
0.91
0.31
0.16
EndoGSLAM
Photo
2.79
0.60
0.30
PPS-Net
NFL
2.93
0.70
0.35
Photo
2.64
0.08
0.04
DA-Hybrid
NFL
8.44
2.42
1.21
Photo
1.16
0.39
1.56
Oracle
NFL
1.22
0.35
0.95
MonoGS
Photo
2.30
0.65
1.19
PPS-Net
NFL
2.45
0.76
1.04
Photo
5.44
0.30
1.64
DA-Hybrid
NFL
1.09
0.40
1.65
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Table 9: Results for sequence cecum 12 under review
Method
Depth
BA
ATE, (mm))
ATE,
Chamfer (mm)
Photo
8.13
2.19
0.49
Oracle
NFL
1.15
2.82
0.1 1.
NICE-SLAM
Photo
1.11
2.13
0.
PPS-Net
NFL
7.98
2.82
0.50
Photo
2.32
1.06
0.53
Oracle
NFL
6.75
2.79
1.40
EndoGSLAM
Photo
4.55
1.18
0.59
PPS-Net
NFL
4.55
2.82
1.41
Photo
5.66
0.89
0.45
DA-Hybrid
NFL
9.47
2.25
1.13
Photo
4.04
0.34
1.47
Oracle
NFL
6.84
0.48
1.24
MonoGS
Photo
6.72
2.71
1.74
PPS-Net
NFL
6.52
2.73
1.70
Photo
5.20
0.66
2.18
DA- DA-Hybrid
NFL
4.26
0.32
1.35
Table 10: Results for sequence cecum t3 Lunde_rrviee
Method
Depth
BA
ATE, (mm) ATE,
Chamfer (mm)
Photo
3.46
2.82
0.11
Oracle
NFL
3.42
2.82
0.07
NICE-SLAM
Photo
2.80
2.64
0.17
PPS-Net
NFL
3.83
2.59
0.14
Photo
0.75
0.15
0.08
Oracle
NFL
0.74
0.27
0.14
EndoGSLAM
Photo
0.79
0.16
0.08
PPS-Net
NFL
2.18
0.22
0.11
Photo
1.45
0.62
0.31
DA-Hybrid
NFL
1.52
0.18
0.09
Photo
0.36
0.16
1.23
Oracle
NFL
0.87
0.31
0.62
MonoGS
Photo
1.07
0.17
1.12
PPS-Net
NFL
1.24
0.14
0.81
Photo
1.06
0.33
0.95
DA- DA-Hybrid
NFL
1.16
0.30
0.93
19

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Table 11: Results for sequence desc 14 p2 under review
Method
Depth
BA
ATE, (mm)
ATE,
Chamfer (mm)
Photo
15.90
2.65
0.59
Oracle
NFL
8.88
2.75
0.50
NICE-SLAM
Photo
0.87
2.80
0.10
PPS-Net
NFL
0.68
2.80
0.10
Photo
0.36
0.56
0.28
Oracle
NFL
0.26
0.94
0.47
EndoGSLAM
Photo
0.48
0.58
0.29
PPS-Net
NFL
1.00
0.83
0.42
Photo
0.45
1.16
0.58
DA-Hybrid
NFL
1.80
2.81
1.40
Photo
0.25
1.05
0.76
Oracle
NFL
0.13
0.98
0.67
MonoGS
Photo
0.49
1.78
0.77
PPS-Net
NFL
0.61
1.67
0.70
Photo
0.69
2.63
0.80
DA- -AbHybrid
NFL
0.20
0.97
0.77
Table 12: Results for sequence sigmoid_ (3 under_reviee
Method
Depth
BA
ATE, (mm)
ATE,
Chamfer (mm)
Photo
1.04
2.47
0.05
Oracle
NFL
0.84
2.83
0.05
NICE-SLAM
Photo
9.47
2.79
0.28
PPS-Net
NFL
6.60
2.55
0.35
Photo
4.81
1.14
0.57
Oracle
NFL
4.11
2.69
1.35
EndoGSLAM
Photo
3.13
1.40
0.70
PPS-Net
NFL
9.82
2.56
1.29
Photo
8.51
2.36
1.18
DA-Hybrid
NFL
8.15
2.80
1.41
Photo
1.44
0.46
1.17
Oracle
NFL
1.19
2.33
0.67
MonoGS
Photo
1,20
1.53
4.63
PPS-Net
NFL
1.22
2.22
0.89
Photo
6.88
2.18
1.42
DA- DA-Hybrid
NFL
1.10
0.67
1.33
20

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Table 13: Results for sequence trans 12 under review
Method
Depth
BA
ATE, (mm))
ATE,
Chamfer (mm)
Photo
0.77
2.83
0.05
Oracle
NFL
0.94
2.80
0.05
NICE-SLAM
Photo
9.18
2.82
0.26
PPS-Net
NFL
9.85
2.81
0.31
Photo
4.21
1.80
0.90
Oracle
NFL
0.49
2.82
1.41
EndoGSLAM
Photo
10.05
1.66
0.84
PPS-Net
NFL
0.90
1.46
0.73
Photo
9.14
2.77
1.39
DA-Hybrid
NFL
14.59
2.18
1.10
Photo
14.38
1.42
1.49
Oracle
NFL
1.51
2.72
0.97
MonoGS
Photo
14.64
1.91
1.52
PPS-Net
NFL
3.08
1.02
1.09
Photo
15.18
2.12
1.68
DA- DA-Hybrid
NFL
9.51
1.41
1.45
Table 14: Results for sequence trans_te (as___t___uuudhe under_revien
Method
Depth
BA
ATE, (mm)
ATE,
Chamfer (mm)
Photo
0.97
2.81
0.15
Oracle
NFL
6.26
2.79
0.68
NICE-SLAM
Photo
3.78
2.80
0.22
PPS-Net
NFL
1.12
2.65
0.13
Photo
0.17
2.70
1.35
Oracle
NFL
0.20
2.70
1.35
EndoGSLAM
Photo
0.30
2.80
1,40
PPS-Net
NFL
0.50
2.81
1.41
Photo
0.46
2.81
1.41
DA-Hybrid
NFL
0.33
2.74
1.37
Photo
0.31
2.67
0.76
Oracle
NFL
0.26
2.66
0.60
MonoGS
Photo
0.33
2.79
0.89
PPS-Net
NFL
0.61
2.83
0.82
Photo
0.29
2.81
0.91
DA- DA-Hybrid
NFL
0.38
2.71
0.71
21

---

Table 15: Results for sequence trans_t4 aatss_t___uuderreeeeeeee mader_review
Method
Depth
BA
ATE, (mm)
(")
Chamfer (mm)
Photo
0.39
2.81
0.05
Oracle
NFL
0.23
2.83
0.05
NICE-SLAM
Photo
7.26
2.77
0. 0.14
PPS-Net
NFL
7.88
2.73
0.23
Photo
2.64
1.47
0.74
Oracle
NFL
1.99
2.02
1.01
EndoGSLAM
Photo
2.02
1.73
0.86
PPS-Net
NFL
2.58
1.78
0.89
Photo
2.99
2.08
1.04
DA-Hybrid
NFL
10.67
2.72
1.37
Photo
1.23
2.41
0.83
Oracle
NFL
0.76
2.06
0.62
MonoGS
Photo
1.12
2.10
0.89
PPS-Net
NFL
1.70
1.80
0.86
Photo
2.30
2.46
1.10
DA- DA-Hybrid
NFL
1.14
2.36
0.85
22

---

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29


Reviews:
[{"review_id": "MeUGkvxYMs", "reviewer": "Reviewer_moAc", "summary": "This paper introduces the Near-Field Light Bundle Adjustment (NFL-BA) loss function, a general loss function that can be injected to improve the performance of neural SLAM systems in environments with some scenarios, like dynamic, co-located near-field lighting. Specifically, NFL-BA optimizes the surface geometry and the camera parameters such that the rendered image has shading variations that match the relative distance and orientation between the surface and the camera. The experiment demonstrates the effectiveness of the proposed NFL-BA, particularly in endoscopic procedures and indoor scenes captured with a moving flashlight.", "questions": "1. How does the NFL-BA perform on longer test sequences?\n2. Can you provide a more detailed analysis of how the improved rendered loss contributes to the overall system when depth input is available?\n3. How does NFL-BA compare with simpler baseline methods, such as using only the central part of the image for optimization in scenarios with flashlight usage?", "limitations": "Some limitations are discussed, and I am also curious about the issues listed in Q1 and Q2.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n* The paper presents a simple yet effective BA loss design in order to address the challenges of dynamic lighting in neural SLAM.\n* The proposed NFL-BA can be applied as a plug-in replacement for the existing photometric bundle adjustment in NeRF-based and 3DGS-based methods. \n* Several datasets and setups are tested to show the effectiveness of the proposed method. \n\n**Weaknesses:**\n* The sequences used for evaluation are relatively short. Longer test sequences are expected to provide a more robust validation of the method.\n* From Table 1, with the input depth, the improvement over existing methods is not significant. This raises concerns about how much the improved rendered loss contributes to the overall system when depth input is available. Given that both NeRF-based and 3DGS-based methods are generally not robust without depth input, this issue is crucial for the practical applicability of the proposed method.\n* I think there may be a simple baseline comparison. For instance, a more straightforward approach could involve using only the central part of the image during the optimization process for scenarios where a flashlight is used.\n* The ATE results in terms of translation and rotation in Table 1 are consistent. More explanation is expected. \n* Minor points: 1) It is hard to compare the results in Fig. 7. 2) The SOTA labels in Table 2 are incomplete.", "was_revised": true, "final_justification": "The rebuttal addressed most of my concerns. The new experiments with the center-crop baseline and long sequences further demonstrate the advantages of the proposed method. So I will keep the positive rating.", "rebuttal": "## Evaluation of Center-Crop Baseline\nWe thank the reviewer for suggesting the center-crop baseline. We compared MonoGS optimized on only the central 75% and 50% of each frame against full-frame MonoGS + NFL-BA on two sequences, measuring translational ATE (ATE_T), Chamfer distance (CD), and reconstructed point count.\n\nTable A: Center-cropping does improve trajectory error upon the original baseline, but suffers from map sparsity, while full-frame NFL-BA maintains both accuracy and dense reconstructions.\n| Method              | trans\\_t3\\_a ATE\\_T | trans\\_t3\\_a CD | trans\\_t3\\_a Points | desc\\_t4\\_p2 ATE\\_T | desc\\_t4\\_p2 CD | desc\\_t4\\_p2 Points |\n| ------------------- | ------------------: | --------------: | ------------------: | ------------------: | --------------: | ------------------: |\n| **MonoGS + NFL-BA** |            **0.26** |        **0.60** |          **28,196** |            **0.13** |        **0.67** |           **8,879** |\n| MonoGS (full frame) |                0.31 |            0.76 |               7,095 |                0.25 |            0.76 |               6,398 |\n| MonoGS (75% center) |                0.35 |            1.06 |               5,761 |                **0.13** |            0.97 |               4,329 |\n| MonoGS (50% center) |                0.40 |            2.82 |               1,865 |                0.15 |            1.56 |               2,100 |\n\n\nAlthough **center-cropping improves camera tracking performance (ATE_t)** of MonoGS and gets close to NFL-BA, it results in **significantly worse reconstruction in terms of quality (CD) and density** (number of points). This is because for camera localization, not all the pixels are essential, and focusing only on central pixels in the image can eliminate the effects of near-field lighting. In contrast, for reconstruction, all pixels in the image matter. This shows the necessity for developing a principled mechanism for handling dynamic near-field lighting, as presented by our work NFL-BA.\n\n## NFL-BA Performance Variation with Length of Test Sequence\n\nWe thank the reviewer for suggesting a thorough evaluation on extended trajectories. C3VD's Seq2 spans  over 4,000 frames with only ground-truth poses, allowing us to measure cumulative drift over time. We note that Seq2 is a screening video that was not utilized in the original paper.\n\nTable B: We evaluate ATE_T (cm) on C3VD Sequence 2 at varying lengths. NFL-BA matches or beats Photo-BA on short sequences and **demonstrates long-term stability**.\n\n| BA         | 500 frames | 1 000 frames | 2 000 frames | 4 000 frames |\n| ---------- | ---------: | -----------: | -----------: | -----------: |\n| Photo  |   2.599 cm |     **1.937 cm** |     8.459 cm |    14.790 cm |\n| **NFL-BA** |   **2.422 cm** |     2.254 cm |     **5.742 cm** |    **11.368 cm** |\n \n     \nWe observe that NFL-BA matches Photometric BA on short sequences but increasingly outperforms it as trajectory length grows. As a **plug-and-play bundle-adjustment loss**, NFL-BA **inherits the stability of the underlying SLAM system** while further enhancing long-term robustness under dynamic near-field lighting.\n\n## Performance of NFL-BA with Ground-truth Depth\n\nWe acknowledge that NFL-BA’s improvement over existing methods is modest when ground truth depth is available, as reported in Table 1 of the main paper. When **ground truth depth is available, the optimization objective becomes easier.** Hence, **inaccurate modeling of photometric BA loss matters less**. In comparison, when ground truth depth is not present, the optimization is challenging and inaccurate photometric modeling leads to larger error.\n\nIn the case of EndoGSLAM, it is a **highly specialized endoscopic SLAM system** with **multiple engineered priors**, including constant‑velocity motion assumptions and brightness‑dependent masking. Unlike methods that rely primarily on photometric cues, EndoGSLAM leverages a diverse set of cues and domain‑specific heuristics. When ground‑truth depth is available, EndoGSLAM already performs extremely well, leaving minimal room for improvement. When the depths are perfectly aligned, NFL‑BA’s shading‑based optimization contributes little and may occasionally conflict with the built-in priors. However, it is heavily tuned for C3VD and **struggles with out‑of‑domain data** such as Colon10K (Figure 7), where motion is less uniform and specular reflections are more prevalent. \n\nAdditionally, we would like to address the inconsistencies with translational and rotational ATE. Near-field lighting produces large changes in pixel intensity for small shifts in camera position, making the loss highly sensitive to translational errors. By contrast, rotational misalignments produce subtler shading differences. As a result, our loss yields larger relative gains on translation than on rotation.\n\n## Evaluation Without Depth Initialization \n\nTo quantify how NFL-BA contributes under varying depth guidance, we evaluated MonoGS on the C3VD dataset with four depth initialization modes: no depth (RGB-only), DaHybrid, PPSNet, and Oracle. We compared Photometric BA against NFL-BA and reported both tracking and reconstruction results.\n\nTable C: Quantitative results on the C3VD dataset with 4 depth inputs including RGB-only. Replacing Photometric BA with NFL-BA significantly improves tracking and mapping **regardless of input depth guidance.**\n\n| Method | Depth Init | BA      | ATE\\_T (mm) | ATE\\_R (°) | Chamfer (mm) |    LPIPS |\n| ------ | ---------- | ------- | ----------: | ---------: | -----------: | -------: |\n| MonoGS | No depth   | Photo   |        2.85 |       1.85 |         3.95 |     0.61 |\n|        |            | **NFL** |    **1.36** |   **0.78** |     **1.50** | **0.48** |\n| MonoGS | DaHybrid   | Photo   |        4.63 |       1.69 |         1.34 |     0.52 |\n|        |            | **NFL** |    **2.35** |   **1.14** |     **1.13** |     0.52 |\n| MonoGS | PPSNet     | Photo   |        3.48 |       1.70 |         1.59 |     0.56 |\n|        |            | **NFL** |    **2.18** |   **1.65** |     **0.99** |     **0.53** |\n| MonoGS | Oracle     | Photo   |        2.90 |       1.11 |         1.16 |     **0.50** |\n|        |            | **NFL** |    **1.60** |   **1.49** |     **0.79** |     0.51 |\n\n\nOverall, NFL-BA yields substantial reductions in both translational and rotational errors across all depth initialization modes. Additionally, map quality consistently improves, demonstrating that our lighting-aware loss provides complementary information to geometric depth cues, enhancing SLAM robustness even when low-quality depth is available.\n\n### Minor Points\n\nFigure 7 contains results for the colon10k dataset, which contains endoscopy videos captured on real patients in clinical setting, which has no ground truth annotations. To see the source videos or point clouds for the MonoGS results, please check out our supplementary submission site. Additionally, we will ensure that all SOTA labels are complete in Table 2 in the final submission."}, {"review_id": "y9wnTiQF9t", "reviewer": "Reviewer_64q3", "summary": "Addressing on the assumption of static, distant illumination, the paper proposes a BA loss that explicitly models dynamic near-field lighting (NFL) by incorporating light intensity fall-off based on the relative distance and orientation between the surface and the co-located light and camera. By replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking on the C3VD colonoscopy dataset.", "questions": "Please response to the weakness part.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1.A Near-Field Lighting BA Loss that explicitly models near-field lighting as a part of BA loss to enable better performance for scenes captured with dynamic lighting.\n\n2.NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations.\n\nWeaknesses\n\n1.Implementation for different SLAM systems. In the Supplementary Materials, the paper provides the detials of coefficientsλren andλgeo, which shows different values in different SLAM systems. In my opinion, this may be a limitation for actual applications. Meanwhile，a consequent question is that are the coefficients different for different datasets?\n\n2.Results on colonoscopy dataset. The experimental results focus on the on colonoscopy dataset. Two aspects of explanations can be presented. \n\n(1)The motivation for usage of colonoscopy dataset. For the compared SLAM systems, like MonoGS, EndoGS and NICE-SLAM, they are all tested on the general indoor scenes. Why authors select the colonoscopy dataset as the main testing dataset.\n\n(2)The results on TUM RGB-D and Replica. The compared SLAM systems all report the results on TUM RGB-D and Replic. It is interesting that can the proposed Near-Field Lighting BA improve localization and redenrring performance on these two datasets?", "was_revised": true, "final_justification": "After reading the rebuttal and considering the comments of other reviewers, I decide to keep the original rating (Borderline accept).", "rebuttal": "## Choice of Hyperparameters\nWe appreciate the reviewer’s concern and would like to clarify that we did **not** tune the hyperparameters {λ_ren, λ_geo} individually for different SLAM frameworks. Instead, we adopted the same values used by the respective base methods. Neural SLAM systems such as MonoGS, EndoGS, and NICE-SLAM inherently use different hyperparameter settings for photometric loss (λ_ren​) and geometric loss (λ_geo​), both in the tracking and mapping stages, due to differences in their underlying 3D scene representations and optimization strategies (e.g., keyframe selection, voxel grid resolution).\n\nImportantly, **we did not modify or tune these values for different datasets either**. The same set of hyperparameters was used consistently across all evaluation sequences and datasets—including C3VD and our in-the-wild captures.\nThis design choice reflects our goal of making **NFL-BA a plug-and-play bundle adjustment loss**, compatible with existing neural SLAM pipelines without requiring any additional tuning. By relying on the default settings of each framework, we demonstrate that NFL-BA can be seamlessly integrated and consistently improve performance across diverse settings.\n\n## NFL-BA on scenes captured with static lighting (TUM RGB-B, Replica)\n\nNear-Field Lighting Effects occur when a light source moves very close to the surface.  In endoscopy, the light source and camera move together within millimeters of the tissue, producing a drastic inverse-square fall-off, deep shadows, and specular highlights. These dynamic lighting phenomena are more extreme in endoscopy than in typical settings. General indoor SLAM datasets (TUM RGB-D, Replica) **assume far-field or static lighting and thus do not exhibit the dynamic illumination** patterns that NFL-BA is designed to correct. Hence, applying NFL-BA on these datasets will not make any sense.\n\nNote that NFL-BA is designed as a plug-and-play Bundle Adjustment loss for any Neural SLAM algorithm, both NeRF & 3DGS-based. This means for any SLAM pipeline, we can easily replace Photometric Bundle Adjustment designed for static lighting with NFL-BA designed for dynamic near-field lighting, without any need to change the SLAM framework.\n\n## Motivation for using the colonoscopy dataset for benchmarking\n\nEndoscopy provides a **highly challenging near-field lighting scenario**, making it an ideal testbed to demonstrate NFL-BA’s efficacy.\nEndoscopy is a minimally invasive imaging technique where a slender and flexible tube equipped with a light and camera is used to inspect internal organs and tissues of the body via an opening, e.g. mouth or anus. Endoscopy is routinely used for cancer screening, e.g. in colons and lungs; for identifying potential issues in the digestive tract, e.g. ulcers, polyps, hemorrhoids; and respiratory problems, e.g. infections, an abnormal narrowing of the airway, and collapsed lungs, and many others. Robust 3D reconstruction and camera tracking from endoscopy enable various downstream applications: (i) improving 3D visualization during an endoscopy procedure and providing guidance to unsurveyed regions, (ii) automatic quantification of various geometric measures of airway or colon to assist in abnormality detection, and (iii) enabling assisted or automatic navigation of the endoscope without tissue damage. \nSince high-quality publicly available endoscopy datasets for the airway are unavailable, we mainly focused on colonoscopy, since C3VD provides high-quality data with ground-truth."}, {"review_id": "TvWb1LdMs5", "reviewer": "Reviewer_hNJU", "summary": "This paper introduces Near-Field Lighting Bundle Adjustment Loss (NFL-BA), a approach to SLAM that explicitly models dynamic near-field lighting. Traditional SLAM systems typically assume static, distant illumination, which leads to performance degradation in scenarios with co-located light and camera, such as endoscopy, subterranean robotics, and search and rescue in collapsed environments. The dynamic near-field lighting in these situations often causes trobles to traditional SLAM using photometric BA.\n\nNFL-BA addresses this by integrating near-field lighting into the Bundle Adjustment (BA) loss, optimizing surface geometry and camera parameters so that rendered images' shading variations match the relative distance and orientation between the surface and the camera.  \n\nEvaluations show that replacing Photometric Bundle Adjustment (Photo-BA) loss with NFL-BA leads to significant improvements in camera tracking and mapping performance. For instance, it improves MonoGS camera tracking by 37% and EndoGS by 14%, achieving state-of-the-art performance on the C3VD colonoscopy dataset.", "questions": "Why NFL-BA degrades the performance on EndoGSLAM with GT depth?", "limitations": "discussed in weakness", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- integrating near-field lighting into the Bundle Adjustment (BA) loss and improved the SLAM in endoscope scenarios.\n- paper is well-written and well-evaluated\n\nWeaknesses:\n\n- while the results are significant, the method is a bit simple\n- the performance on EndoGSLAM is not good\n- code is not accessible", "was_revised": true, "final_justification": "The author addressed my concerns and considering on the contribution I would maintain my score as weakly accept.", "rebuttal": "## Impact of NFL-BA on EndoGSLAM\n\nEndoGSLAM is a **highly specialized SLAM system** tailored for the C3VD dataset through a set of carefully **engineered priors**, such as constant-velocity motion assumptions and brightness-dependent masking, that together deliver outstanding performance in a very specific setting.\n\n**Dataset specialization:** EndoGSLAM is heavily tuned for C3VD and struggles with out‑of‑domain data such as Colon10K (Figure 7), where motion is less uniform and specular reflections are more prevalent. For both test sequences, EndoGSLAM completely fails to produce a continuous structure due to the absence of ground truth depth and broken assumptions\n\n**Engineered for Ground Truth depth:** When ground‑truth depth is available, EndoGSLAM already performs extremely well, leaving minimal room for improvement. When the depths are perfectly aligned, NFL‑BA’s shading‑based optimization contributes little and may occasionally conflict with the built-in priors. However, under noisy depths, EndoGSLAM performance drops sharply, and NFL‑BA provides significant gains by leveraging physically‑based lighting cues to stabilize pose and reconstruction.\n\nIn short, the small degradation under GT depth reflects EndoGSLAM’s rigidity and high baseline rather than a fundamental weakness of NFL‑BA. In typical clinical scenarios, NFL‑BA offers a clear benefit.\n\n## Perceived Simplicity of NFL‑BA\n\nAlthough NFL-BA is conceptually straightforward, its novelty is in **embedding a physically grounded near-field lighting model directly into a differentiable bundle-adjustment loss**. To our knowledge, no prior Bundle Adjustment (BA) formulation explicitly accounts for dynamic, co-located illumination. This reformulation not only captures essential lighting physics but also **remains stable during optimization.** Moreover, the method’s minimal implementation overhead makes it a **drop-in enhancement** for a wide range of neural SLAM systems without altering their core architectures. Despite its simplicity, NFL-BA delivers state-of-the-art results, reducing MonoGS’s absolute trajectory error by 37% and EndoGS’s by 14% on the challenging C3VD colonoscopy dataset.\n\n### Code Availability\n\nWe understand the importance of reproducibility and will release the full codebase upon acceptance."}, {"review_id": "QC9aZIWmRH", "reviewer": "Reviewer_BMQ8", "summary": "This paper proposes a novel Near-Field Lighting Bundle Adjustment (NFL-BA) loss to address the performance degradation of SLAM systems under dynamic near-field lighting conditions (e.g., endoscopy, underground exploration). By explicitly integrating a near-field lighting attenuation model into the bundle adjustment loss of neural rendering-based SLAM, our method decouples surface albedo from lighting intensity, significantly improving the accuracy of camera pose estimation and scene reconstruction. On the C3VD colonoscopy dataset, NFL-BA reduces MonoGS’s tracking error by 37% (Table 2) and achieves an average 35% improvement in pose estimation in indoor scenes captured with smartphone flashlights (Table 3). Experiments demonstrate that NFL-BA is a universal and effective plug-and-play solution.", "questions": "1. How does NFL-BA address specular reflections (e.g., from intestinal fluid/mucus) that violate the pure diffuse assumption?\n2. Why is ignoring angular attenuation (β=0) valid for smartphone flashlights? Does this overestimate irradiance near surfaces?\n3, How robust is NFL-BA without depth inputs (e.g., monocular RGB-only) or in low-texture/low-light scenes?", "limitations": "As mentioned by authors, it is currently limited in handling specular reflections, sub-surface scattering, and inter-reflections.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. This work systematically tackles dynamic lighting interference in SLAM scenarios where the camera and light source move together (e.g., endoscopy, rescue robots), addressing a critical gap in traditional SLAM methods that assume static far-field lighting. The proposed lighting-geometric decoupling model separates surface albedo from near-field lighting attenuation (inverse-square law and normal vector angle), enhancing robustness to dynamic shadows (Equations 5–7).\n\n2. NFL-BA seamlessly integrates into implicit (e.g., NICE-SLAM) and explicit (e.g., 3D Gaussian Splatting) neural rendering SLAM frameworks by replacing the Photometric BA loss (Equation 3) without requiring modifications to other components. Its versatility and practicality are validated across medical datasets (C3VD, Colon10K) and general scenarios (smartphone flashlight indoor scenes).\n\n3. The experiments are comprehensive, with clear visualizations. On the C3VD dataset, NFL-BA consistently outperforms baselines: MonoGS’s tracking error drops by 37% (3.48 mm → 2.18 mm), and EndoGSLAM’s rotational error decreases by 38% (1.81° → 1.13°) (Tables 1–2). Real-world tests on colonoscopy data (Colon10K) and dynamic indoor lighting scenarios (Figure 7, Table 3) further confirm its practicality, with an average 35% reduction in pose errors.\n\nWeaknesses:\n1. The current dynamic lighting model has limitations. For instance, the model described in Section 4.1 (Equations 5–7) is based on a pure diffuse reflection assumption. However, specular highlights caused by intestinal fluid and mucus (e.g., reflections on mucosal surfaces) are not accounted for. This may lead to brightness distortion in liquid-containing regions, where specular highlights could be misinterpreted as high albedo.\n\n2. While setting β=0 is reasonable for endoscopic scenarios (where inverse-square attenuation dominates), it remains unclear whether ignoring angular attenuation in wide-angle light sources (e.g., smartphone flashlights) could overestimate near-surface irradiance and compromise pose-tracking accuracy. Further clarification is needed.\n\n3. Experimentally, the robustness of NFL-BA in extreme scenarios requires additional validation. For example, Table 2 could include pure monocular SLAM experiments without depth input (e.g., using RGB only). Additionally, Tables 1–3 should report error ranges from multiple trials (e.g., “2.18±0.15 mm”) to demonstrate statistical stability.", "was_revised": true, "final_justification": "One core issue I concerned have been addressed.", "rebuttal": "## Lighting Model Limitations\n\nWe agree with the reviewer, and we clarified in the paper that the NFL-BA formulation only models diffuse reflectance and direct illumination, ignoring specular reflectance, subsurface scattering and inter-reflections. While these effects are important, physically modeling them in an optimization framework is **significantly challenging and an open problem** in the community. This is because **these effects introduce non-differentiable elements** (e.g., path-tracing for inter-reflections), **highly non-convex functions** (e.g., sub-surface scattering and specular reflections modelled as Spatially Varying BRDF), and additional parameters for material modeling.\n\nDuring experimentation on real data with specular reflections (both in endoscopy and in-the-wild captures), we first **detect and exclude specular regions** via a simple intensity mask. We convert each frame to grayscale and discard pixels above a 0.9 intensity threshold, filtering out bright highlights. While this approach handles most specular artifacts, it cannot correct for reflective surfaces beyond the mask. The C3VD phantom dataset and self-captured in-the-wild dataset have predominantly matte surfaces, so this works well. However, as noted, the Colon10K clinical endoscopy dataset contains significantly more specular highlights, leading to moderate degradation in reconstruction. We believe future research is required to explicitly account for specular reflections in the presence of dynamic lighting.\n\n## Angular Attenuation\n\nWe appreciate the reviewer’s point that setting ignoring angular attenuation (β=0) may overestimate irradiance under wide-angle lighting such as smartphone flashlights. While β=0 is physically justified for tightly collimated endoscopic LEDs, we validated this choice on two self-captured indoor sequences with varying β.\n\nTable A: Ablation of the angular-attenuation coefficient on two self-captured indoor sequences, reporting translational ATE and LPIPS across β ∈ {0.00, 0.25, 0.50, 0.75, 1.00}. \n| Scene   | Metric |    β = 0.00 |     β = 0.25 |     β = 0.50 |     β = 0.75 |     β = 1.00 |\n| ------- | ------ | ----------: | -----------: | -----------: | -----------: | -----------: |\n| Guitars | ATE\\_T | 0.17 ± 0.01 | 0.17 ± 0.003 | 0.16 ± 0.006 | 0.17 ± 0.006 | 0.17 ± 0.011 |\n|         | LPIPS  | 0.36 ± 0.02 | 0.37 ± 0.006 | 0.40 ± 0.030 | 0.36 ± 0.017 | 0.36 ± 0.020 |\n| Porch   | ATE\\_T | 0.33 ± 0.16 | 0.25 ± 0.013 | 0.27 ± 0.045 | 0.36 ± 0.158 | 0.22 ± 0.012 |\n|         | LPIPS  | 0.50 ± 0.03 | 0.53 ± 0.008 | 0.48 ± 0.005 | 0.51 ± 0.035 | 0.49 ± 0.010 |\n\n\n\nWe found that β=0 provides competitive mean accuracy with the low variance, confirming our default choice. Non-zero β values **can** yield marginal improvements in ATE or LPIPS, but variability increases and our coarse search did not pinpoint a universally optimal value.\n\nGiven the minimal gains versus added complexity of tuning β, we **retain β=0 in our current formulation**. In future work, we intend to explore learning β as a per-scene or per-dataset parameter to enable fine-grained angular attenuation without extensive manual search.\n\n## Experiment Robustness\n\nWe have included updated tables with averages and standard deviations for  5 runs per trial. Due to lack of time and resources during the rebuttal period, we only included evaluations for EndoGSLAM on C3VD, and MonoGS on Self-Capture. We will include all 5 trials, for all experiments, in the final version.\n\nTable 1. Quantitative Evaluation on the C3VD under dynamic lighting, comparing EndoGSLAM with standard Photo-BA versus NFL-BA.\n| depth  | BA    | ATE_T | ATE_R| Chamfer |\n| ------ | ----- | ----- | ---- | ----- |\n| Oracle | Photo | **1.92 ± 0.02** | 1.81 ± 0.02 | **0.85 ±  0.01** |\n|        | NFL   | 2.04 ± 0.06 | **1.12 ± 0.09** | 1.00 ±  0.01 |\n| PPS    | Photo | 3.12 ± 0.14 | 1.76 ± 0.07 | 1.22 ± 0.01 |\n|        | NFL   | **2.56 ± 0.27** | **1.23 ± 0.03** | **1.19 ± 0.01** |\n| DA-H   | Photo | 5.76 ± 1.26 | 2.22 ± 0.12 | **2.03 ± 0.38** |\n|        | NFL   | **4.75 ± 1.15** | **1.81 ± 0.31** | 2.12 ± 0.45 |\n\nTable 2: Quantitative results on four self-captured indoor scenes under dynamic lighting, comparing MonoGS with standard Photo-BA versus NFL-BA\n\n| Scene   | BA    |          ATE\\_T |           LPIPS |\n| ------- | ----- | --------------: | --------------: |\n| Guitars | Photo |     0.29 ± 0.02 |     0.39 ± 0.02 |\n|         | NFL   | **0.17 ± 0.01** | **0.36 ± 0.02** |\n| Porch   | Photo |     0.53 ± 0.04 |     0.51 ± 0.02 |\n|         | NFL   | **0.33 ± 0.16** | **0.50 ± 0.03** |\n| Pool    | Photo |     0.46 ± 0.04 |     0.47 ± 0.01 |\n|         | NFL   | **0.32 ± 0.04** | **0.46 ± 0.02** |\n| Stairs  | Photo |     0.37 ± 0.04 |     0.43 ± 0.03 |\n|         | NFL   | **0.22 ± 0.03** | **0.34 ± 0.02** |\n\n### Full MonoGS Evaluation\n\nAs suggested, we evaluated MonoGS with RGB-only input and compiled all MonoGS experiments below. Overall, NFL-BA yields **substantial reductions in both translational and rotational errors across all depth initialization modes**. Additionally, map quality consistently improves, demonstrating that our lighting-aware loss provides complementary information to geometric depth cues, enhancing SLAM robustness even when low-quality depth is available.\n\nTable 3: Quantitative results on the C3VD dataset with 4 depth inputs including RGB-only. Replacing Photometric BA with NFL-BA significantly improves tracking and mapping **regardless of input depth guidance.**\n| Method | Depth Init | BA      | ATE\\_T (mm) | ATE\\_R (°) | Chamfer (mm) |    LPIPS |\n| ------ | ---------- | ------- | ----------: | ---------: | -----------: | -------: |\n| MonoGS | No depth   | Photo   |        2.85 |       1.85 |         3.95 |     0.61 |\n|        |            | **NFL** |    **1.36** |   **0.78** |     **1.50** | **0.48** |\n| MonoGS | DaHybrid   | Photo   |        4.63 |       1.69 |         1.34 |     0.52 |\n|        |            | **NFL** |    **2.35** |   **1.14** |     **1.13** |     0.52 |\n| MonoGS | PPSNet     | Photo   |        3.48 |       1.70 |         1.59 |     0.56 |\n|        |            | **NFL** |    **2.18** |   **1.65** |     **0.99** |     **0.53** |\n| MonoGS | Oracle     | Photo   |        2.90 |       1.11 |         1.16 |     **0.50** |\n|        |            | **NFL** |    **1.60** |   **1.49** |     **0.79** |     0.51 |"}]

Author rebuttal:

Meta-review: This paper addresses the performance degradation of SLAM under dynamic near-field lighting by proposing a new bundle adjustment loss, NFL-BA. As a plug-and-play replacement for existing photometric BA, NFL-BA incorporates a physics-based light attenuation model and can be broadly applied to both NeRF-based and 3DGS-based SLAM. Experiments on endoscopic datasets (C3VD, Colon10K) and scenes captured with smartphone flashlights demonstrate improvements in both camera tracking and mapping accuracy.

While some reviewers noted the simplicity of the method as a potential weakness, this same property was also recognized as a strength, since NFL-BA can enhance existing SLAM systems with minimal modifications. Concerns raised included: (1) the lack of validation on standard SLAM benchmarks, (2) the assumption of a purely diffuse reflection model, which does not account for specularities or subsurface scattering, and (3) only modest improvements in scenarios with strong priors or reliable depth input. However, the authors addressed these points in the rebuttal by providing additional experiments, including long-sequence evaluations, comparisons with a center-crop baseline, and analyses across different depth initialization settings.

Overall, the reviewers maintained or strengthened their positive assessments, confirming both the practicality and the impact of the proposed approach.
Decision: Accept (poster)