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Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting.

Andrea Dunn Beltran1, Daniel Rho1, Marc Niethammer2

  • 1University of North Carolina at Chapel Hill.

Advances in Neural Information Processing Systems
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Near-Field Lighting Bundle Adjustment Loss (NFL-BA) to improve Simultaneous Localization and Mapping (SLAM) performance in dynamic, near-field lighting conditions. NFL-BA enhances camera tracking and mapping accuracy, particularly in challenging environments like endoscopy.

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Area of Science:

  • Robotics and Computer Vision
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Simultaneous Localization and Mapping (SLAM) systems often fail in dynamic, near-field lighting conditions due to view-dependent shading.
  • Real-world applications like endoscopy and subterranean robotics require robust SLAM in the absence of external lighting.

Purpose of the Study:

  • To develop a novel loss function, Near-Field Lighting Bundle Adjustment Loss (NFL-BA), for enhancing SLAM performance under dynamic, near-field lighting.
  • To integrate NFL-BA into neural rendering-based SLAM systems for improved camera tracking and mapping.

Main Methods:

  • Explicitly modeling near-field lighting within the Bundle Adjustment loss function.
  • Integrating NFL-BA into existing SLAM frameworks, such as MonoGS and EndoGS.
  • Evaluating performance on colonoscopy datasets (C3VD) and indoor scenes with on-camera flash.

Main Results:

  • Significant improvements in camera tracking accuracy (37% for MonoGS, 14% for EndoGS) compared to traditional Photometric Bundle Adjustment loss.
  • Achieved state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset.
  • Demonstrated substantial SLAM performance gains in indoor scenes with on-camera flash.

Conclusions:

  • NFL-BA effectively addresses the challenges posed by dynamic near-field lighting in SLAM.
  • The proposed method offers significant benefits for autonomous navigation and 3D visualization in endoscopic procedures.
  • NFL-BA provides a robust solution for improving SLAM accuracy across various applications with challenging lighting conditions.