RSGaussian:3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis

• URL: http://arxiv.org/abs/2412.18380v1
• Date: Tue, 24 Dec 2024 12:08:50 GMT
• Title: RSGaussian:3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis
• Authors: Yiling Yao, Wenjuan Zhang, Bing Zhang, Bocheng Li, Yaning Wang, Bowen Wang,
• Abstract summary: RSGaussian is an innovative novel view synthesis (NVS) method for aerial remote sensing scenes. It incorporates LiDAR point cloud as constraints into the 3D Gaussian Splatting method, which ensures that Gaussians grow and split along geometric benchmarks. The approach also introduces coordinate transformations with distortion parameters for camera models to achieve pixel-level alignment between LiDAR point clouds and 2D images.
• Score: 6.900071309404811
• License:
• Abstract: This study presents RSGaussian, an innovative novel view synthesis (NVS) method for aerial remote sensing scenes that incorporate LiDAR point cloud as constraints into the 3D Gaussian Splatting method, which ensures that Gaussians grow and split along geometric benchmarks, addressing the overgrowth and floaters issues occurs. Additionally, the approach introduces coordinate transformations with distortion parameters for camera models to achieve pixel-level alignment between LiDAR point clouds and 2D images, facilitating heterogeneous data fusion and achieving the high-precision geo-alignment required in aerial remote sensing. Depth and plane consistency losses are incorporated into the loss function to guide Gaussians towards real depth and plane representations, significantly improving depth estimation accuracy. Experimental results indicate that our approach has achieved novel view synthesis that balances photo-realistic visual quality and high-precision geometric estimation under aerial remote sensing datasets. Finally, we have also established and open-sourced a dense LiDAR point cloud dataset along with its corresponding aerial multi-view images, AIR-LONGYAN.

Related papers

• GP-GS: Gaussian Processes for Enhanced Gaussian Splatting [10.45038376276218]
  This paper proposes a novel 3D reconstruction framework that achieves adaptive and uncertainty-guided densification of sparse SfM point clouds. The pipeline utilizes uncertainty estimates to guide the pruning of high-variance predictions. Experiments conducted on synthetic and real-world datasets validate the effectiveness and practicality of the proposed framework.
  arXiv  Detail & Related papers  (2025-02-04T12:50:16Z)
• RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering [13.684624443214599]
  We present RDG-GS, a novel sparse-view 3D rendering framework with Relative Depth Guidance based on 3D Gaussian Splatting. The core innovation lies in utilizing relative depth guidance to refine the Gaussian field, steering it towards view-consistent spatial geometric representations. Across extensive experiments on Mip-NeRF360, LLFF, DTU, and Blender, RDG-GS demonstrates state-of-the-art rendering quality and efficiency.
  arXiv  Detail & Related papers  (2025-01-19T16:22:28Z)
• G2SDF: Surface Reconstruction from Explicit Gaussians with Implicit SDFs [84.07233691641193]
  We introduce G2SDF, a novel approach that integrates a neural implicit Signed Distance Field into the Gaussian Splatting framework. G2SDF achieves superior quality than prior works while maintaining the efficiency of 3DGS.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• 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. 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)
• Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis [53.702118455883095]
  We propose a novel method for synthesizing novel views from sparse views with Gaussian Splatting. Our key idea lies in exploring the self-supervisions inherent in the binocular stereo consistency between each pair of binocular images. Our method significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-24T15:10:27Z)
• Mode-GS: Monocular Depth Guided Anchored 3D Gaussian Splatting for Robust Ground-View Scene Rendering [47.879695094904015]
  We present a novelview rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome the limitations of existing 3D Gaussian splatting algorithms. Our method results in improved rendering performance, based on PSNR, SSIM, and LPIPS metrics, in ground scenes with free trajectory patterns.
  arXiv  Detail & Related papers  (2024-10-06T23:01:57Z)
• Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)
• LiDAR-guided Stereo Matching with a Spatial Consistency Constraint [7.448893867145506]
  This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM) The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Our formulation expands the constraint of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels.
  arXiv  Detail & Related papers  (2022-02-21T02:29:19Z)

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.