Reconstruction Using the Invisible: Intuition from NIR and Metadata for Enhanced 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2508.14443v1
• Date: Wed, 20 Aug 2025 05:58:07 GMT
• Title: Reconstruction Using the Invisible: Intuition from NIR and Metadata for Enhanced 3D Gaussian Splatting
• Abstract summary: Agricultural scenes present unique challenges for 3D reconstruction methods.<n>We introduce textbfNIRPlant, a novel multimodal dataset encompassing Near-Infrared (NIR) imagery, RGB imagery, textual metadata, Depth, and LiDAR data.<n>We propose textbfNIRSplat, an effective multimodal Gaussian splatting architecture employing a cross-attention mechanism combined with 3D point-based positional encoding.
• Score: 2.1684193267057603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While 3D Gaussian Splatting (3DGS) has rapidly advanced, its application in agriculture remains underexplored. Agricultural scenes present unique challenges for 3D reconstruction methods, particularly due to uneven illumination, occlusions, and a limited field of view. To address these limitations, we introduce \textbf{NIRPlant}, a novel multimodal dataset encompassing Near-Infrared (NIR) imagery, RGB imagery, textual metadata, Depth, and LiDAR data collected under varied indoor and outdoor lighting conditions. By integrating NIR data, our approach enhances robustness and provides crucial botanical insights that extend beyond the visible spectrum. Additionally, we leverage text-based metadata derived from vegetation indices, such as NDVI, NDWI, and the chlorophyll index, which significantly enriches the contextual understanding of complex agricultural environments. To fully exploit these modalities, we propose \textbf{NIRSplat}, an effective multimodal Gaussian splatting architecture employing a cross-attention mechanism combined with 3D point-based positional encoding, providing robust geometric priors. Comprehensive experiments demonstrate that \textbf{NIRSplat} outperforms existing landmark methods, including 3DGS, CoR-GS, and InstantSplat, highlighting its effectiveness in challenging agricultural scenarios. The code and dataset are publicly available at: https://github.com/StructuresComp/3D-Reconstruction-NIR

Related papers

• NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation [65.34304674634713]
  We introduce NG-GS, a framework for high-quality object segmentation in 3DGS that explicitly addresses boundary discretization.<n>We show that our method achieves photorealistic state-of-the-art performance, with significant gains in boundary mIoU.
  arXiv  Detail & Related papers  (2026-04-16T07:14:07Z)
• LightHarmony3D: Harmonizing Illumination and Shadows for Object Insertion in 3D Gaussian Splatting [90.15229815175593]
  We present LightHarmony3D, a novel framework for illumination-consistent mesh insertion in 3DGS scenes.<n>By leveraging generative priors instead of iterative optimization, our method efficiently captures dominant scene illumination.<n>We introduce the first dedicated benchmark for mesh insertion in 3DGS, providing a standardized evaluation framework for assessing lighting consistency and photorealism.
  arXiv  Detail & Related papers  (2026-03-31T03:26:30Z)
• SWIR-LightFusion: Multi-spectral Semantic Fusion of Synthetic SWIR with Thermal IR (LWIR/MWIR) and RGB [6.054759773528757]
  We propose a synthetically generate SWIR-like structural/contrast cues without claiming spectral reproduction.<n>We then propose a multimodal fusion framework integrating synthetic SWIR, LWIR, and RGB modalities.<n>The outcomes highlight substantial potential for real-world applications in surveillance and autonomous systems.
  arXiv  Detail & Related papers  (2025-10-15T11:00:41Z)
• GSFusion:Globally Optimized LiDAR-Inertial-Visual Mapping for Gaussian Splatting [20.665306154754564]
  We propose GSFusion, an online LiDAR-Inertial-Visual mapping system that ensures high-precision map consistency through a surfel-to-surfel constraint in the global pose-graph optimization.<n> Experiments on public and our datasets demonstrate our system outperforms existing 3DGS SLAM systems in terms of rendering quality and map-building efficiency.
  arXiv  Detail & Related papers  (2025-07-31T06:15:51Z)
• TVG-SLAM: Robust Gaussian Splatting SLAM with Tri-view Geometric Constraints [22.121665995381324]
  TVG-SLAM is a robust RGB-only 3DGS SLAM system that leverages a novel tri-view geometry paradigm to ensure consistent tracking and high-quality mapping.<n>Our method improves tracking robustness, reducing the average Absolute Trajectory Error (ATE) by 69.0% while achieving state-of-the-art rendering quality.
  arXiv  Detail & Related papers  (2025-06-29T12:31:05Z)
• Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting [95.61137026932062]
  Intern-GS is a novel approach to enhance the process of sparse-view Gaussian splatting.<n>We show that Intern-GS achieves state-of-the-art rendering quality across diverse datasets.
  arXiv  Detail & Related papers  (2025-05-27T05:17:49Z)
• Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [53.2590751089607]
  Real-IAD D3 is a high-precision multimodal dataset that incorporates an additional pseudo3D modality generated through photometric stereo.<n>We introduce an effective approach that integrates RGB, point cloud, and pseudo-3D depth information to leverage the complementary strengths of each modality.<n>Our experiments highlight the importance of these modalities in boosting detection robustness and overall IAD performance.
  arXiv  Detail & Related papers  (2025-04-19T08:05:47Z)
• Diffusion-Guided Gaussian Splatting for Large-Scale Unconstrained 3D Reconstruction and Novel View Synthesis [22.767866875051013]
  We propose GS-Diff, a novel 3DGS framework guided by a multi-view diffusion model to address limitations of current methods.<n>By generating pseudo-observations conditioned on multi-view inputs, our method transforms under-constrained 3D reconstruction problems into well-posed ones.<n> Experiments on four benchmarks demonstrate that GS-Diff consistently outperforms state-of-the-art baselines by significant margins.
  arXiv  Detail & Related papers  (2025-04-02T17:59:46Z)
• MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction [84.07233691641193]
  We introduce MonoGSDF, a novel method that couples primitives with a neural Signed Distance Field (SDF) for high-quality reconstruction.<n>To handle arbitrary-scale scenes, we propose a scaling strategy for robust generalization.<n>Experiments on real-world datasets outperforms prior methods while maintaining efficiency.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels [51.08794269211701]
  We introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results.<n>3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS.
  arXiv  Detail & Related papers  (2024-11-19T11:59:54Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [53.58528891081709]
  We present LiDAR-GS, a real-time, high-fidelity re-simulation of LiDAR scans in public urban road scenes.<n>The method achieves state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving [9.023864430027333]
  We propose a 3D Gaussian Splatting-based multimodal place recognition network dubbed GPSR.<n>It explicitly combines multi-view RGB images and LiDAR point clouds into atemporally unified scene representation with the Multimodal Gaussian Splatting.<n>Our method can effectively leverage complementary strengths of both multi-view cameras and LiDAR, achieving SOTA place recognition performance while maintaining solid generalization ability.
  arXiv  Detail & Related papers  (2024-10-01T00:43:45Z)
• GauU-Scene V2: Assessing the Reliability of Image-Based Metrics with Expansive Lidar Image Dataset Using 3DGS and NeRF [2.4673377627220323]
  We introduce a novel, multimodal large-scale scene reconstruction benchmark that utilizes newly developed 3D representation approaches. GauU-Scene encompasses over 6.5 square kilometers and features a comprehensive RGB dataset coupled with LiDAR ground truth. We are the first to propose a LiDAR and image alignment method for a drone-based dataset.
  arXiv  Detail & Related papers  (2024-04-07T08:51:31Z)
• Ternary-Type Opacity and Hybrid Odometry for RGB NeRF-SLAM [58.736472371951955]
  We introduce a ternary-type opacity (TT) model, which categorizes points on a ray intersecting a surface into three regions: before, on, and behind the surface. This enables a more accurate rendering of depth, subsequently improving the performance of image warping techniques. Our integrated approach of TT and HO achieves state-of-the-art performance on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-12-20T18:03:17Z)

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.