Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark

• URL: http://arxiv.org/abs/2607.29684v1
• Date: Fri, 31 Jul 2026 17:59:18 GMT
• Title: Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark
• Abstract summary: This paper offers a new perspective for RGB-NIR low-light imaging by incorporating 3D-aware neural modeling.<n>A powerful model can be optimized to implicitly fuse extremely noisy RGB observations with NIR cues in 3D space, effectively recovering clean RGB images.
• Score: 68.49637363691325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust low-light imaging remains challenging for the community. Recent studies have explored fusing Near-Infrared (NIR) with noisy RGB to achieve improved enhancement, yet most methods depend on carefully curated training data pairs, with limited robustness under different scenarios. This paper offers a new perspective for RGB-NIR low-light imaging by incorporating 3D-aware neural modeling. Without using clean RGB supervision, a powerful model can be optimized to implicitly fuse extremely noisy RGB observations with NIR cues in 3D space, effectively recovering clean RGB images. The proposed model obviates the requirement for clean RGB data collection, generalizes across different noise levels. Extensive evaluations on synthetic and real data demonstrate its superiority. Codes available: https://github.com/MyNiuuu/3DarkFusion

Related papers

• SEAR: Simple and Efficient Adaptation of Visual Geometric Transformers for RGB+Thermal 3D Reconstruction [28.61594700250136]
  We propose SEAR, a strategy that adapts a pretrained geometry transformer to multimodal RGB-T inputs.<n>Our approach significantly outperforms state-of-the-art methods for 3D reconstruction and camera pose estimation.<n>We validate our architecture through extensive ablation studies, demonstrating how the model aligns both modalities.
  arXiv  Detail & Related papers  (2026-03-19T11:27:21Z)
• Towards RAW Object Detection in Diverse Conditions [65.30190654593842]
  We introduce the AODRaw dataset, which offers 7,785 high-resolution real RAW images with 135,601 annotated instances spanning 62 categories. We find that sRGB pre-training constrains the potential of RAW object detection due to the domain gap between sRGB and RAW. We distill the knowledge from an off-the-shelf model pre-trained on the sRGB domain to assist RAW pre-training.
  arXiv  Detail & Related papers  (2024-11-24T01:23:04Z)
• NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset [53.79524776100983]
  Leveraging near-infrared (NIR) images to assist visible RGB image denoising shows the potential to address this issue.<n>Existing works still struggle with taking advantage of NIR information effectively for real-world image denoising.<n>We propose an efficient Selective Fusion Module (SFM), which can be plug-and-played into the advanced denoising networks.
  arXiv  Detail & Related papers  (2024-04-12T14:54:26Z)
• All in One: RGB, RGB-D, and RGB-T Salient Object Detection [6.417439550842723]
  Aient object detection (SOD) aims to identify the most attractive objects within an image. Previous researches have focused on saliency detection with individual data type. We propose an innovative model framework that provides a unified solution for the salient object detection task of three types of data.
  arXiv  Detail & Related papers  (2023-11-23T03:34:41Z)
• DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation [76.81628995237058]
  DFormer is a novel framework to learn transferable representations for RGB-D segmentation tasks. It pretrains the backbone using image-depth pairs from ImageNet-1K. DFormer achieves new state-of-the-art performance on two popular RGB-D tasks.
  arXiv  Detail & Related papers  (2023-09-18T11:09:11Z)
• DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior [6.162654963520402]
  High-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. We propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP) Based on the deep structures from both RGB and NIR domains, we introduce the DIP to leverage the structure inconsistency to guide the fusion of RGB-NIR.
  arXiv  Detail & Related papers  (2023-03-13T03:31:29Z)
• Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images [89.81919625224103]
  Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. We present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection.
  arXiv  Detail & Related papers  (2022-01-01T03:02:27Z)
• BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning [26.24526760567159]
  We propose a novel bi-modal (RGB-D) panorama synthesis framework. We focus on indoor environments where the RGB-D panorama can provide a complete 3D model for many applications. Our method synthesizes high-quality indoor RGB-D panoramas and provides realistic 3D indoor models.
  arXiv  Detail & Related papers  (2021-12-12T08:20:01Z)
• Synergistic saliency and depth prediction for RGB-D saliency detection [76.27406945671379]
  Existing RGB-D saliency datasets are small, which may lead to overfitting and limited generalization for diverse scenarios. We propose a semi-supervised system for RGB-D saliency detection that can be trained on smaller RGB-D saliency datasets without saliency ground truth.
  arXiv  Detail & Related papers  (2020-07-03T14:24:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.