Segmentation-Guided Neural Radiance Fields for Novel Street View Synthesis

• URL: http://arxiv.org/abs/2503.14219v1
• Date: Tue, 18 Mar 2025 12:54:36 GMT
• Title: Segmentation-Guided Neural Radiance Fields for Novel Street View Synthesis
• Authors: Yizhou Li, Yusuke Monno, Masatoshi Okutomi, Yuuichi Tanaka, Seiichi Kataoka, Teruaki Kosiba,
• Abstract summary: We propose a segmentation-guided enhancement to Neural Radiance Fields (NeRF) for outdoor street scenes.<n>Our approach extends ZipNeRF and utilizes Grounded SAM for segmentation mask generation.<n>We also introduce appearance embeddings to adapt to inconsistent lighting across view sequences.
• Score: 11.110535737604302
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in Neural Radiance Fields (NeRF) have shown great potential in 3D reconstruction and novel view synthesis, particularly for indoor and small-scale scenes. However, extending NeRF to large-scale outdoor environments presents challenges such as transient objects, sparse cameras and textures, and varying lighting conditions. In this paper, we propose a segmentation-guided enhancement to NeRF for outdoor street scenes, focusing on complex urban environments. Our approach extends ZipNeRF and utilizes Grounded SAM for segmentation mask generation, enabling effective handling of transient objects, modeling of the sky, and regularization of the ground. We also introduce appearance embeddings to adapt to inconsistent lighting across view sequences. Experimental results demonstrate that our method outperforms the baseline ZipNeRF, improving novel view synthesis quality with fewer artifacts and sharper details.

Related papers

• Exploiting Deblurring Networks for Radiance Fields [12.4982871657283]
  We propose DeepDeblurRF, a novel radiance field deblurring approach that can synthesize high-quality novel views from blurred training views. We conduct extensive experiments on both camera motion blur and defocus blur, demonstrating that DeepDeblurRF achieves state-of-the-art novel-view synthesis quality with significantly reduced training time.
  arXiv  Detail & Related papers  (2025-02-20T11:11:18Z)
• NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild [55.154625718222995]
  We introduce NeRF On-the-go, a simple yet effective approach that enables the robust synthesis of novel views in complex, in-the-wild scenes. Our method demonstrates a significant improvement over state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-05-29T02:53:40Z)
• NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections [57.63028964831785]
  Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. We address these issues with an approach based on ray tracing. Instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts rays from these points and traces them through the NeRF representation to render feature vectors.
  arXiv  Detail & Related papers  (2024-05-23T17:59:57Z)
• HarmonicNeRF: Geometry-Informed Synthetic View Augmentation for 3D Scene Reconstruction in Driving Scenarios [2.949710700293865]
  HarmonicNeRF is a novel approach for outdoor self-supervised monocular scene reconstruction. It capitalizes on the strengths of NeRF and enhances surface reconstruction accuracy by augmenting the input space with geometry-informed synthetic views. Our approach establishes new benchmarks in synthesizing novel depth views and reconstructing scenes, significantly outperforming existing methods.
  arXiv  Detail & Related papers  (2023-10-09T07:42:33Z)
• Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes [62.769186261245416]
  We present a novel inverse rendering framework for large urban scenes capable of jointly reconstructing the scene geometry, spatially-varying materials, and HDR lighting from a set of posed RGB images with optional depth. Specifically, we use a neural field to account for the primary rays, and use an explicit mesh (reconstructed from the underlying neural field) for modeling secondary rays that produce higher-order lighting effects such as cast shadows.
  arXiv  Detail & Related papers  (2023-04-06T17:51:54Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• Urban Radiance Fields [77.43604458481637]
  We perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings. Each of these three extensions provides significant performance improvements in experiments on Street View data.
  arXiv  Detail & Related papers  (2021-11-29T15:58:16Z)
• NeRF++: Analyzing and Improving Neural Radiance Fields [117.73411181186088]
  Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings. NeRF fits multi-layer perceptrons representing view-invariant opacity and view-dependent color volumes to a set of training images. We address a parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, 3D scenes.
  arXiv  Detail & Related papers  (2020-10-15T03:24:14Z)

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.