Title: NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
Venue: NeurIPS 2025
Accepted: yes
Keywords: SLAM

Endoscopy

Near-Field lighting
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.

MonoGS

Abstract

Camera Tracking Camera Tracking Camera Tracking Camera Tracking Camera Tracking Camera Tracking 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 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 in SLAM performance due to NFL-BA.

1 Introduction

Simultaneous Localization and Mapping (SLAM) enables autonomous agents to build a spatial map of an unknown environment while estimating their own poses within it, with wide-ranging applications in robotics, computer vision, autonomous vehicles, and scientific imaging. Most SLAM systems [38, 34, 7, 58, 60, 56, 49, 20, 27, 16] assume an autonomous agent navigating an 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 an error between the captured image and the re-rendered image using estimated 3D scene and camera poses.

Ground-Truth

Ground-Truth

Ground-Truth

Ground-Truth

Ground-Truth

Ground-Truth

Near-Field Light Bundle Adjustment w. NFL-BA

Near-Field Light Bundle Adjustment w. NFL-BA

MonoGS

MonoGS

MonoGS

MonoGS

MonoGS

MonoGS

MonoGS

MonoGS

MonoGS

MonoGS

GT

Photo-BA

Photo-BA

NFL-BA

GT

Photo

Photo

Static Lighting

Dynamic Lighting

GT

GT

NFL

0.20

2.4

'z

~ 2.0

~ 2.0

1.6

1.2

1.6

0.8

0.4

X (m)

0'0

13.H

0.8

0.4

X (m)

0.0

1.2

8'O-

0.4

1.6

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 is used to inspect internal organs such as the airway and the colon [9, 45, 33, 23, 51, 17]. Accurate trajectory estimation is crucial for reliably guiding instruments to areas of interest, mapping anomalies, and avoiding tissue damage during navigation. In subterranean search-and-rescue or collapsed-building 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 it. 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-based 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-Field 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-art 3DGS-based SLAM systems, general-purpose MonoGS [27] and endoscopy-specific EndoGSLAM [45], and one neural implicit SLAM, NICE-SLAM [58], by replacing their Photometric Bundle Adjustment loss with NFL-BA 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, we also demonstrate the effectiveness of NFL-BA on indoor rooms captured with a 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, 11]. With advancements in neural scene representations

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, 16, 10, 53].

These dense SLAM approaches all rely on a 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 to their 3D counterparts by minimizing reprojection error, assuming static lighting and Lambertian surfaces [14]. Although effective in controlled environments, it struggles in complex or low-texture scenes. Photometric BA (Photo-BA) [1] incorporates pixel intensities into the optimization process, minimizing photometric re-projection errors and proving advantageous in environments where feature matching fails [11]. 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 Stereo [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 [35] demonstrated the effectiveness of near-field lighting to enhance depth estimation. LightNeus [3] exploited the 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 intrinsics, exposure, and scene depths to normalize brightness variations in real time [11] while probabilistic SLAMs with unscented filtering further stabilizes pose estimates under uncertain lighting conditions [26]. More recently, learning-based matchers [22, 48] adapt descriptors to cope with complex lighting variations. None of these methods, however, explicitly model near-field lighting geometry to handle this co-located light setting.

SLAM in endoscopy. Early works [40, 12] demonstrated the feasibility of applying SLAM in such environments 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 [36, 29, 18]. However these techniques often struggle with challenging sequences from both synthetic and clinical data. Recently, neural rendering-based methods [39, 23, 45, 51, 13, 15] have proved especially effective in generating high-quality details and modeling textureless regions with a large number of Gaussians. In this work, we 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 rendering-based SLAM. We represent the camera at time t by its extrinsics P t = [ R t , T t ] ∈ SE (3) and known intrinsics K , yielding the projection π t = KP t . We assume the camera intrinsic K to be the same for all frames and known or calibrated ahead of time. Pixels are denoted p and 3D camera-space points by x .

In neural rendering, scene parameters Θ , whether in the form of neural networks or primitives, encode visual and geometric information, such as colors c i and occupancy α i . Given Θ and P t , we can get the color ˆ C ( · ) and the depth ˆ D ( · ) of a pixel p from a frame at time t as follows [28, 20]:

where N denotes the group of samples for a pixel p , with α i representing the occupancy of the i -th sample, and z i denotes its distance from the camera center.

To optimize P t and Θ , dense SLAM methods typically use rendering loss L ren , reducing the rendering errors between the rendered and captured images [58, 27, 49] and, if estimated or ground

truth depth maps are available, an additional depth loss L geo can be added [43]. Typically, these losses take the form of L p norm as follows with variations with M t as a pixel-wise mask:

Bundle adjustment optimizes both P t and Θ using the following combined loss:

where W denotes the set of frames used for the bundle adjustment and the hyperparameters λ ren and λ geo 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 Θ and P t are optimized over a set 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 [41, 58, 44, 60, 38] uses a set of neural networks F ( x, d ; Θ) → ( c i , σ i ) , optimized to estimate the color c i and the volume density σ i for an input 3D coordinate x and the view direction d . The the occupancy can be calculated from the volume density σ i and the distance between adjacent samples δ i as α i = 1 -exp( -σ i δ i ) .

3D Gaussian Splatting. For 3D Gaussian Splatting [20] SLAM methods, the scene is represented by a set of Gaussians with mean µ i , covariance Σ i in world space, color c i , and opacity α i . The shape parameters and occupancy α i of the splatted 2D Gaussians are computed as follows:

where J t is the Jacobian of the projection π t , p denotes a pixel coordinate, and ¯ µ i t , ¯ Σ i t are the splatted mean and covariance of Gaussian G i in pixel space.

4 Near-Field Light Bundle Adjustment

We introduce a novel Near-Field Lighting based Bundle Adjustment loss, NFL-BA, that integrates near-field lighting with neural-rendering 3D scene representations to improve performance 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

Co-located light

and camera

Incoming Radiance

Surface Normals

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 as the camera and the light moves through the scene. In this setting, the 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 is inaccurate for dynamic near-field lighting as it doesn't separate effects of illumination due to camera movement from the intrinsic view-independent color of the scene, i.e. albedo.

Our goal is to explicitly model surface appearance as albedo and separate near-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. 4.1 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 a function of scene geometry and camera pose. Then in sec. 4.2, we will use this image formation to create the Near-Field Bundle Adjustment loss and show how it can be easily integrated into neural rendering based SLAM framework.

Ground Truth

Albedo

Normals

Reconstructed

PPS

SI-MSE

RGB Images

Error

Features from input Image and Depth

0.8

0.6

0.4

0.2

00

4.1 Image Formation with Near-Field Lighting

We consider an image-formation model under near-field lighting for a single image following previous works [19, 35]. Each pixel p and the corresponding three-dimensional point x p in the camera space receives different light intensities and directions, characterized by the light source to surface direction L d ( · ) and attenuation term L a ( · ) , as follows:

where x L is the location of the light source, f is the forward (optical axis) vector. β is an angular attenuation coefficient, and will be discussed in sec. 4.2.

Assuming a diffuse reflectance model, which has proven effective for depth estimation in endoscopic scenes [35], we can approximate the rendered image at each pixel ˆ C ( · ) as:

where ρ ( · ) and n ( · ) are albedo and normal at position x p of pixel p respectively. PPS ( · ) is a per-pixel shading term. Note that existing approaches that uses this near-field light image formation model [19, 35] uses pixel-based representation to predict depth map or surface geometry from images captured from a 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 i in eq. 3) as the product of the albedo ρ ( · ) and the shading term PPS ( · ) , 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 pixel-space. This leads to the modified rendering equation under near-field lighting:

Note that eq. 6 represents a special case of eq. 7 where a single sample is considered and the occupancy α i 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 β in eq. 5 to zero. This effectively ignores the directional fall-off component, reducing the light attenuation term to a simple inverse-square fall-off L a ( x p ) = 1 / ∥ x p -x L ∥ 2 . This simplification is justified because the angular attenuation in settings like endoscopy is often negligible compared to the inverse square law attenuation, and estimating β accurately can be challenging due to variations in endoscope designs. In future work, we plan determine the optimal value of β for different systems and incorporate the light direction vector r e t for more accurate modeling which can further improve camera rotation during Bundle Adjustment.

Empirical validation of our image-formation model on colonoscopy image. Fig. 4 provides an example colonoscopy image from the C3VD dataset [4], showing the accuracy of the near-light field model (Eq. 6) with β of 0. Albedo was estimated by converting each RGB image to HSV color

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 using the near-field lighting based image formation model defined in eq . 7 expressed as follows:

where ˆ C pps denotes the rendered image with near-field lighting-incorporated volumetric rendering equation (Eq. 7). This reformulation seamlessly integrates near-field lighting cues into the neural rendering framework without altering the rest of the SLAM framework. Since our 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 a linear space. To ensure consistency, we apply an inverse gamma correction of γ = 2 . 2 to the sRGB images before computing PPS , or equivalently, gamma-correct the linear PPS output by γ = 1 / 2 . 2 when rendering back to 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 n ( · ) from neural fields, we utilize the direction of the gradient of the occupancy with respect to the spatial coordinates as follows [5]: n ( x i ) = -∇ σ ( x i ) / ∥∇ σ ( x i ) ∥ . For Gaussian Splatting, we use the shortest axis of each Gaussian as its normal, following [52, 8, 47]. In both cases, we ensure the computed normal is oriented towards the camera by enforcing n ( x ) ⊤ L d ( x ) to be positive. Otherwise, we flip the normals by multiplying them by -1 for stability.

5 Evaluation

Our proposed method is a plug-in approach that can be applied to any existing neuralrendering-based SLAM framework. We first test our method on endoscopy videos using one neural implicit SLAM, NICE-SLAM [58], as well as two existing 3DGS-SLAM frameworks: the general-purpose MonoGS [27] and the endoscopy-specific EndoGSLAM [45]. In each case, we replace the standard Photometric Bundle Adjustment loss (3) with our proposed equation NFL-BA loss (8). Additionally, we also test MonoGS [27] on self-captured indoor scenes with a co-located light and camera.

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) a 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 synthetic materials to simulate realistic tissue geometry. Colon10K. To test generalization in realworld clinical endoscopy settings, we evaluate on Colon10K [24], a large-scale video dataset without depth or 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 a 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.

Table 1: Quantitative Evaluation on the C3VD [4] dataset with oracle depth map. Replacing Photometric BA with NFL-BA significantly improves tracking quality of two state-of-the-art 3D Gaussian SLAMs, MonoGS [27] and EndoGS [59], and one neural implicit SLAM, NICE-SLAM [58].

procedures and are typically around 300-600 frames. This setting is significantly more challenging than the phantom setting, since frames may contain motion blur, specular highlights, and fluid occlusions. Sequences are uniformly sampled and fisheye corrected.

Self-Captured Indoor Scenes. To study the role of near-field lighting in a 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 geometry and reflectance (diffuse, specular), imaged under dynamic motion. Scenes are captured using a co-located point light source mounted to camera. Ground-truth camera trajectories were recorded via motion capture, but no reference point clouds are available; hence, we report only trajectory error (ATE t ) and perceptual quality (LPIPS), omitting Chamfer distance. Details and additional visualizations are included in the supplementary.

Photo

Oracle

PPSNet

DA-Hybrid

desc_t4_a

sigmoid_t3_a

trans_t2_a

Photo

Photo

Photo

Photo

Photo

Photo

Photo

Photo

GT

GT

NFL 2.54

NFL 0.36

107.5

110

=56

>112.5

72

115.0

40.0

00

0.0

15.0

GT

NFL 4.55

GT

NFL 0.47

Base 1.27

NFL 1.50

107.5

110.

112.5

115.0

72.5

176

NFL 5.66

GT

NFL 0.43

GT

NFL 2.87

-110.

-112.5

115.0

77.5

Metrics. For evaluation, we basically followed other neural rendering SLAM algorithms [27, 45]. For tracking performance, we measure the root mean square error of the Absolute Trajectory Error (ATE) for both translation and rotation across all frames. Translation error ATE t is in millimeters (mm) for the endoscopy scenes and meters (m) for the in-door scenes. And rotation error ATE r is in degrees. To assess the mapping quality, we use the Chamfer distance from ground truth point clouds to the nearest points in the estimated point clouds [46], for more details please see supplementary. In addition, we evaluate rendering quality using the 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 a single NVIDIA RTX A6000 GPU. The per-scene optimization takes ∼ 1 FPS. For more information on runtime speed, please see supplementary materials.

trans_t2_a

desc_t4_a

sigmoid_t3_a

Oracle

PPSNet

DA-Hybrid

Photo

Photo

Photo

Photo

Photo

Photo

Photo

Photo

Photo

L

GT

LD

NFL

1.51

NFL0.13

NFL1.19

-109

90

111.

112.

100

210

39.0

120

200

72.0

X (mm)

90

180

190

73.5

X(mm)

75.0

96.0

X (mm)

65

110

19

GT

NFL

3.08

V

NFL

0.61

10

NFL

1.22

11C

-112

100

210

120

110

06

180

55

LD

NFL

9.51

NFL0.20

NFL

1.10

40 111.0

100

112.5

-105

210

17.5

110

80

39.0

Oracle

PPSNet

DPT-Hybrid

Ground

Truth

0.95 mm

1.04 mm

1.64 mm

1.16 mm

1.12 mm

1.64 mm

Oracle

PPSNet

DPT-Hybrid

Ground

Truth

0.97 mm

1.45 mm

1.09 mm

1.52 mm

1.44 mm

1.68 mm

NFL-BA

Photo-BA

trans_t2_a

cecum_t1_a

Photo-BA

NFL-BA

Seq. 4

Seq. 3

EndoGSLAM

5.2 Evaluation on C3VD Endoscopy Data

Because NFL-BA is designed as a 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. 1 we replace Photometric Bundle Adjustment loss with NFL-BA loss for depth is initialized with ground-truth or oracle. NFL-BA significantly improves camera localization ( ATE t ) and mapping for NICE-SLAM and MonoGS, and only camera rotation ( ATE r ) 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[27] and EndoGSLAM [45] for depth maps we use PPSNet [35], a state-of-the-art monocular depth estimation algorithm for endoscopy, and fine-turned general-purpose depth estimator, which we will call it as DA-Hybrid - DepthAnything[50] with DINOv2 encoder [32]. NFL-BA significantly improves camera localization ( ATE t ) and camera rotation ( ATE r ) for both MonoGS [27] and EndoGSLAM [45] while producing similar rendering quality. For example, camera localization for MonoGS is improved by 37% for PPSNet and 49% for 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 metrics, 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 4 in Fig. 8. EndoGSLAM fails to construct any real structure, with many disconnected regions along a 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 'down-the-barrel' sequence exposes a clear cylindrical lumen. With Photo-BA, MonoGS captures the overall shape but produces a broken segment due to trajectory drift. NFL-BA corrects this, yielding a continuous 'hollow-center' reconstruction. Minor artifacts from extreme specular highlights remain (green points), as detailed in the supplement.

Sequence 3. In the extended traversal, both Photo-BA and NFL-BA recover the colon'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).

5.4 Evaluation on Indoor Scene

To validate NFL-BA in a non-medical setting, we evaluate on four indoor scenes. Table 3 shows that replacing standard Photometric BA with NFL-BA yields substantial reductions in ATE t across

GT

Guitars

Porch

0.0

0.5

0.0

0.5

Photo-BA

NFL-BA

GT

Photo-BA

NFL-BA

Photo-BA

NFL-BA

Photo-BA

NFL-BA

Photo

Photo

LD

NFL0.18

2.1

f'z

3.0

9

GT

NFL0.35

S'Z

X (m)

3.5

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 to 0.20m (44%) in Stair . On average, NFL-BA reduces tracking error by ∼ 35% , 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. 9).

6 Conclusions

In this paper, we presented a novel bundle adjustment loss that explicitly models dynamic nearfield 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 is 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 SLAM methods, improving performance on a challenging endoscopy dataset and indoor scenes captured with a phone camera with a flashlight turned on.

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-reflections. Incorporating a more complex image formulation is beyond the scope of the current work, and addressing these remains a promising direction for future research.

7 Acknowledgments

This work is supported by a National Institute of Health (NIH) project #R21EB035832 "Next-gen 3D Modeling of Endoscopy Videos" and #R21EB037440 'Gen-AI Airway Simulator for 3D Endoscopy'. We also thank Stephen M. Pizer, Ron Alterovitz, and Dr. Sarah McGill for helpful discussions during the project.

References

Hatem Alismail, Brett Browning, and Simon Lucey. Photometric bundle adjustment for vision-based slam, 2016.

Dejan Azinovi´ c, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies. Neural rgb-d surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 6290-6301, June 2022.

Víctor M Batlle, José MM Montiel, Pascal Fua, and Juan D Tardós. LightNeuS: Neural surface reconstruction in endoscopy using illumination decline. In Medical Image Computing and Computer Assisted Intervention (MICCAI) , 2023.

Taylor L Bobrow, Mayank Golhar, Rohan Vijayan, Venkata S Akshintala, Juan R Garcia, and Nicholas J Durr. Colonoscopy 3d video dataset with paired depth from 2d-3d registration. Medical Image Analysis , page 102956, 2023.

Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, and Hendrik P.A. Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 12684-12694, October 2021.

C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. M. Montiel, and J. D. Tardós. Orb-slam3: An accurate open-source library for visual, visual-inertial, and multi-map slam. IEEE Transactions on Robotics , 37(6):1874-1890, 2021.

Devendra Singh Chaplot, Ruslan Salakhutdinov, Abhinav Gupta, and Saurabh Gupta. Neural topological slam for visual navigation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 12875-12884, 2020.

Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yunsong Wang, and Gim Hee Lee. Vcr-gaus: View consistent depth-normal regularizer for gaussian surface reconstruction. arXiv preprint arXiv:2406.05774 , 2024.

B. Cui, H. Zhang, X. Li, W. Zhou, and T. Cheng. Endodac: Efficient adapting foundation model for self-supervised depth estimation from any endoscopic camera. In Proceedings of the Medical Image Computing and Computer-Assisted Intervention Conference (MICCAI) , 2024.

Tianchen Deng, Yaohui Chen, Leyan Zhang, Jianfei Yang, Shenghai Yuan, Jiuming Liu, Danwei Wang, Hesheng Wang, and Weidong Chen. Compact 3d gaussian splatting for dense visual slam, 2024.

J. Engel, V. Koltun, and D. Cremers. Direct sparse odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence , 40(3):611-625, 2018.

Oscar G. Grasa, Javier Civera, and J. M. M. Montiel. Ekf monocular slam with relocalization for laparoscopic sequences. In 2011 IEEE International Conference on Robotics and Automation , pages 4816-4821, 2011.

Jiaxin Guo, Jiangliu Wang, Di Kang, Wenzhen Dong, Wenting Wang, and Yun-hui Liu. Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction . In proceedings of Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 , volume LNCS 15007. Springer Nature Switzerland, October 2024.

R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision . Cambridge University Press, 2003.

Michel Hayoz, Christopher Hahne, Thomas Kurmann, Max Allan, Guido Beldi, Daniel Candinas, Pablo Márquez-Neila, and Raphael Sznitman. Online 3D reconstruction and dense tracking in endoscopic videos . In proceedings of Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 , volume LNCS 15006. Springer Nature Switzerland, October 2024.

Huajian Huang, Longwei Li, Cheng Hui, and Sai-Kit Yeung. Photo-slam: Real-time simultaneous localization and photorealistic mapping for monocular, stereo, and rgb-d cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024.

Yiming Huang, Beilei Cui, Long Bai, Ziqi Guo, Mengya Xu, Mobarakol Islam, and Hongliang Ren. Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting . In proceedings of Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 , volume LNCS 15006. Springer Nature Switzerland, October 2024.

Raúl Iranzo, Víctor M Batlle, Juan D Tardós, and José MM Montiel. Endometric: Near-light metric scale monocular slam. arXiv preprint arXiv:2410.15065 , 2024.

Y. Iwahori, H. Sugie, and N. Ishii. Reconstructing shape from shading images under point light source illumination. In [1990] Proceedings. 10th International Conference on Pattern Recognition , volume i, pages 83-87 vol.1, 1990.

Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuehler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. , 42(4), July 2023.

Daniel Lichy, Soumyadip Sengupta, and David W. Jacobs. Fast light-weight near-field photometric stereo, 2022.

Philipp Lindenberger, Paul-Edouard Sarlin, and Marc Pollefeys. LightGlue: Local Feature Matching at Light Speed. In ICCV , 2023.

Yifan Liu, Chenxin Li, Chen Yang, and Yixuan Yuan. Endogaussian: Gaussian splatting for deformable surgical scene reconstruction. arXiv preprint arXiv:2401.12561 , 2024.

Ruibin Ma, Sarah K. McGill, Rui Wang, Julian Rosenman, Jan-Michael Frahm, Yubo Zhang, and Stephen Pizer. Colon10k: A benchmark for place recognition in colonoscopy. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) , pages 1279-1283, 2021.

Ruibin Ma, Rui Wang, Stephen Pizer, Julian Rosenman, Sarah K McGill, and Jan-Michael Frahm. Realtime 3d reconstruction of colonoscopic surfaces for determining missing regions. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part V 22 , pages 573-582. Springer, 2019.

R. Martínez-Cantín and J. D. Tardós. Unscented kalman filter for visual slam with photometric calibration. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 1234-1241, 2016.

Hidenobu Matsuki, Riku Murai, Paul H.J. Kelly, and Andrew J. Davison. Gaussian splatting slam. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 18039-18048, June 2024.

Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM , 65(1):99-106, December 2021.

Javier Morlana, Juan D Tardós, and José MM Montiel. Topological slam in colonoscopies leveraging deep features and topological priors. In International Conference on Medical Image Computing and Computer-Assisted Intervention , pages 733-743. Springer, 2024.

R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos. Orb-slam: A versatile and accurate monocular slam system. In IEEE Transactions on Robotics , volume 31, pages 1147-1163, 2015.

Andriy Myronenko and Xubo Song. Point set registration: Coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence , 32(12):2262-2275, 2010.

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research , 2024. Featured Certification.

Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Taylor L. Bobrow, Gulfize Coskun, Kagan Incetan, Yasin Almalioglu, Faisal Mahmood, Eva Curto, Luis Perdigoto, Marina Oliveira, Hasan Sahin, Helder Araujo, Henrique Alexandrino, Nicholas J. Durr, Hunter B. Gilbert, and Mehmet Turan. Endoslam dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos. Medical Image Analysis , 71:102058, 2021.

Albert Palomer, Pere Ridao, and David Ribas. Inspection of an underwater structure using point-cloud slam with an auv and a laser scanner. Journal of field robotics , 36(8):1333-1344, 2019.

Akshay Paruchuri, Samuel Ehrenstein, Shuxian Wang, Inbar Fried, Stephen M Pizer, Marc Niethammer, and Roni Sengupta. Leveraging near-field lighting for monocular depth estimation from endoscopy videos. In Computer Vision - ECCV 2024 , Cham, 2024. Springer Nature Switzerland.

Juan J Gómez Rodríguez, José MM Montiel, and Juan D Tardós. Nr-slam: Non-rigid monocular slam. IEEE Transactions on Robotics , 2024.

Javier Rodríguez-Puigvert*, Víctor M. Batlle*, José María M. Montiel, Rubén Martínez-Cantín, Pascal Fua, Juan D. Tardós, and Javier Civera. LightDepth: single-view depth self-supervision from illumination decline. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023.

Erik Sandström, Yue Li, Luc Van Gool, and Martin R Oswald. Point-slam: Dense neural point cloud-based slam. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 18433-18444, 2023.

Yufei Shi, Beijia Lu, Jia-Wei Liu, Ming Li, and Mike Zheng Shou. Colonnerf: Neural radiance fields for high-fidelity long-sequence colonoscopy reconstruction. arXiv preprint arXiv:2312.02015 , 2023.

D. Stoyanov, G. Mylonas, F. Deligianni, A. Darzi, and G.-Z. Yang. Soft-tissue motion tracking and structure estimation for robotic assisted mis procedures. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) , pages 139-146, 2005.

E. Sucar, S. Liu, J. Ortiz, and A. J. Davison. imap: Implicit mapping and positioning in real-time. In Proceedings of the IEEE International Conference on Computer Vision , pages 6229-6238, 2021.

Zachary Teed and Jia Deng. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems , volume 34, pages 16558-16569. Curran Associates, Inc., 2021.

Fabio Tosi, Youmin Zhang, Ziren Gong, Erik Sandström, Stefano Mattoccia, Martin R Oswald, and Matteo Poggi. How nerfs and 3d gaussian splatting are reshaping slam: a survey. arXiv preprint arXiv:2402.13255 , 4, 2024.

Hengyi Wang, Jingwen Wang, and Lourdes Agapito. Co-slam: Joint coordinate and sparse parametric encodings for neural real-time slam. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 13293-13302, June 2023.

Kailing Wang, Chen Yang, Yuehao Wang, Sikuang Li, Yan Wang, Qi Dou, Xiaokang Yang, and Wei Shen. EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting . In proceedings of Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 , volume LNCS 15006. Springer Nature Switzerland, October 2024.

Shuxian Wang, Yubo Zhang, Sarah K. McGill, Julian G. Rosenman, Jan-Michael Frahm, Soumyadip Sengupta, and Stephen M. Pizer. A surface-normal based neural framework for colonoscopy reconstruction. In Alejandro Frangi, Marleen de Bruijne, Demian Wassermann, and Nassir Navab, editors, Information Processing in Medical Imaging , pages 797-809, Cham, 2023. Springer Nature Switzerland.

Qianyi Wu, Jianmin Zheng, and Jianfei Cai. Surface reconstruction from 3d gaussian splatting via local structural hints. In Computer Vision - ECCV 2024 , Cham, 2024. Springer Nature Switzerland.

Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang, and Lihua Xie. AirSLAM: An efficient and illumination-robust point-line visual slam system. IEEE Transactions on Robotics (TRO) , 2024.

Chi Yan, Delin Qu, Dan Xu, Bin Zhao, Zhigang Wang, Dong Wang, and Xuelong Li. Gs-slam: Dense visual slam with 3d gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 19595-19604, 2024.

Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR , 2024.

Shuojue Yang, Qian Li, Daiyun Shen, Bingchen Gong, Qi Dou, and Yueming Jin. Deform3DGS: Flexible Deformation for Fast Surgical Scene Reconstruction with Gaussian Splatting . In proceedings of Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 , volume LNCS 15006. Springer Nature Switzerland, October 2024.

Keyang Ye, Qiming Hou, and Kun Zhou. 3d gaussian splatting with deferred reflection. In ACM SIGGRAPH 2024 Conference Papers , SIGGRAPH '24, New York, NY, USA, 2024. Association for Computing Machinery.

Vladimir Yugay, Yue Li, Theo Gevers, and Martin R. Oswald. Gaussian-slam: Photo-realistic dense slam with gaussian splatting, 2024.

Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018.

Yubo Zhang, Jan-Michael Frahm, Samuel Ehrenstein, Sarah K McGill, Julian G Rosenman, Shuxian Wang, and Stephen M Pizer. Colde: a depth estimation framework for colonoscopy reconstruction. arXiv preprint arXiv:2111.10371 , 2021.

S. Zhi, J. Lai, A. Kundu, M. Bloesch, A. Davison, and A. Zisserman. Nerf-slam: Real-time dense monocular slam with neural radiance fields. In Proceedings of the European Conference on Computer Vision (ECCV) , 2022.

L. Zhou, R. Klette, and K. Scheibe. A multi-image shape-from-shading framework for near-lighting perspective endoscopes. International Journal of Computer Vision , 82:1-24, 2009.

A. Zhu, Z. Zhang, H. Su, L. Li, G. Wang, and X. Li. Nice-slam: Neural implicit scalable encoding for slam. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022.

Lingting Zhu, Zhao Wang, Jiahao Cui, Zhenchao Jin, Guying Lin, and Lequan Yu. Endogs: Deformable endoscopic tissues reconstruction with gaussian splatting. In M. Emre Celebi, Mauricio Reyes, Zhen Chen, and Xiaoxiao Li, editors, Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 Workshops , pages 135-145, Cham, 2025. Springer Nature Switzerland.

Zihan Zhu, Songyou Peng, Viktor Larsson, Zhaopeng Cui, Martin R Oswald, Andreas Geiger, and Marc Pollefeys. Nicer-slam: Neural implicit scene encoding for rgb slam. In International Conference on 3D Vision (3DV) , March 2024.

Supplementary Materials

Overview of Appendices

Our appendices contain the following additional details:

Appendix A: Dataset Processing and Collection

Appendix B: Implementation Details and Computational Costs

Appendix C: Point Clouds and Metrics

Appendix D: Long Sequence Validation

Appendix E: Evaluation of Center-Crop Baseline

Appendix F: Lighting model and Angular Attenuation

Appendix G: Results on Endoscopy Datasets

A Dataset Processing and Collection

C3VD. We test our method on a subset of 8 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_t1_a, cecum_t2_a, cecum_t3_a, sigmoid_t3_a, desc_t4_a_p2, trans_t2_a, trans_t3_a, and trans_t4_a.

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 a height of 384 pixels while maintaining the aspect ratio, then crop the central region to obtain a 384×384 pixel image.

Colon10k. Since Colon10K provides no ground-truth depths, we compute per-frame estimates with PPSNet. Each image is center-cropped and uniformly resized to 384 × 384 px to match our SLAM 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 are downscaled to 256 × 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, we ran all models on a single NVIDIA RTX A6000 GPU. The per-scene optimization takes approximately 1 FPS. We found that NFL-BA only reduces the fps runtime by a small amount on the C3VD dataset.

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 a 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 λ ren to 0.5 during tracking and 0.2 during mapping, and λ geo to 1 in both phases.

EndoGSLAM. The main difference is the weight map M t in the bundle adjustment loss (Eq. 3) to exclude over-exposed pixels that can arise in endoscopy-specific 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 ATE t went from 3.03 to 3.38). We used the

default loss weights of EndoGSLAM, λ ren ; λ geo , set to 0.5 and 1 during tracking and 1 and 1 during mapping, respectively.

MonoGS. To integrate our method, we treat the Gaussian color features as 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 set λ ren and λ geo to 0.8 and 0.5, respectively.

C Point Clouds and Metrics

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 [31] and the Chamfer distances are also in millimeters.

Coloring Point Clouds. We use 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 c0_full_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 to Photo-BA using the MonoGS backbone in Table 5.

NFL-BA matches Photometric BA on short sequences and increasingly outperforms it as trajectory length grows. As a plug-and-play bundle-adjustment loss, NFL-BA enhances long-term robustness under dynamic near-field lighting.

E Evaluation of Center-Crop Baseline

We compared MonoGS Photo-BA 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 in Table 6.

Although 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 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 a principled mechanism for handling dynamic near-field lighting, as proposed by NFL-BA.

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 an 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 on matte datasets (C3VD, in-the-wild), degradation occurs on Colon10K with more specular highlights. Future work will address specular reflections under dynamic lighting.

Regarding angular attenuation, β = 0 is justified for tightly collimated endoscopic LEDs, we must validated this for non endoscopy scenes. Using NFL-BA on the MonoGS backbone, we validate various β values on two indoor sequences as seen in Table 7

β = 0 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 β = 0 , and plan to explore learning β as a per-scene parameter in future work.

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 run below. Additionally, we include point clouds for EndoGSLAM in Figure 10, more can be found on our project page.

Oracle

PPSNet DPT-Hybrid

Ground

Truth

Ground

Truth

NFL

Base

NFL

Base

DA-Hybrid

0.86 mm

0.80 mm

1.34 mm

1.15 mm

3.90 mm

5.08 mm

0.32 mm

0.26 mm

0.56 mm

0.39 mm

1.02 mm

1.49 mm

NeurIPS Paper Checklist

The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.

Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:

You should answer [Yes] , [No] , or [NA] .

[NA] means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.

Please provide a short (1-2 sentence) justification right after your answer (even for NA).

The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include it (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.

The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation. While "[Yes] " is generally preferable to "[No] ", it is perfectly acceptable to answer "[No] " provided a proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] " or "[NA] " is not grounds for rejection. While the questions are phrased in a binary way, we acknowledge that the true answer is often more nuanced, so please just use your best judgment and write a justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Yes, they are.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: In Sec. 6, we discuss limitations.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: Our paper does not present formal theorems or proofs; it focuses on an engineering loss formulation validated empirically. We include the equations necessay to formulate and justify our loos function but no explicit proof is needed.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: We will put that information in the supplementary due to the page limits.

Guidelines: We provide full algorithmic details (loss terms, hyperparameters, depth preprocessing), dataset splits (C3VD sequences, indoor scenes) in the summplemental material.

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: We are willing to make captured data and code publicly available once accepted.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: They are described in Sec. 5.1.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: Due to page limits, we will provide those in the supplementary.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: It is included in Sec. 5.1.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: Our work conforms with the NeurIPS Code of ethics.

Guidelines: Our work involves algorithmic development and non-sensitive imaging data; no ethical issues beyond standard academic practice. All endoscopy data are publicly available and properly referenced.

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: They are described in Sec. 1.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: There are no high-risk pretrained models or sensitive datasets requiring special access controls.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: We cited all the datasets and included license if possible.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [No]

Justification: We intend to release our code and captured data upon acceptance, but structured documentation (e.g., README, data schema) is not yet provided; this will be included with the release.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: No human participants or crowdsourced annotations were involved.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: Our paper does not involve crowdsourcing or research with human subjects. We only used publicly available data that already passed IRB approvals.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: Large language models were not used for any part of the methodology

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nAs mentioned by authors, it is currently limited in handling specular reflections, sub-surface scattering, and inter-reflections.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

comments: 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.

NFL-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.  

Evaluations 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.

STRENGTHS AND WEAKNESSES:
Strengths:

- integrating near-field lighting into the Bundle Adjustment (BA) loss and improved the SLAM in endoscope scenarios.
- paper is well-written and well-evaluated

Weaknesses:

- while the results are significant, the method is a bit simple
- the performance on EndoGSLAM is not good
- code is not accessible

QUESTIONS:
Why NFL-BA degrades the performance on EndoGSLAM with GT depth?

LIMITATIONS:
discussed in weakness
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAddressing 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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?\n\nQUESTIONS:\nPlease response to the weakness part.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nSome limitations are discussed, and I am also curious about the issues listed in Q1 and Q2.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}