Creating Seamless 3D Maps Using Radiance Fields

• URL: http://arxiv.org/abs/2403.11364v1
• Date: Sun, 17 Mar 2024 22:49:07 GMT
• Title: Creating Seamless 3D Maps Using Radiance Fields
• Authors: Sai Tarun Sathyan, Thomas B. Kinsman,
• Abstract summary: It is desirable to create 3D object models and 3D maps from 2D input images for applications such as navigation, virtual tourism, and urban planning. Traditional methods have difficulty with reflective surfaces and specular reflections. Google Road View does not create an actual 3D object model, only a collection of views.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is desirable to create 3D object models and 3D maps from 2D input images for applications such as navigation, virtual tourism, and urban planning. The traditional methods of creating 3D maps, (such as photogrammetry), require a large number of images and odometry. Additionally, traditional methods have difficulty with reflective surfaces and specular reflections; windows and chrome in the scene can be problematic. Google Road View is a familiar application, which uses traditional methods to fuse a collection of 2D input images into the illusion of a 3D map. However, Google Road View does not create an actual 3D object model, only a collection of views. The objective of this work is to create an actual 3D object model using updated techniques. Neural Radiance Fields (NeRF[1]) has emerged as a potential solution, offering the capability to produce more precise and intricate 3D maps. Gaussian Splatting[4] is another contemporary technique. This investigation compares Neural Radiance Fields to Gaussian Splatting, and describes some of their inner workings. Our primary contribution is a method for improving the results of the 3D reconstructed models. Our results indicate that Gaussian Splatting was superior to the NeRF technique.

Related papers

• DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data [50.164670363633704]
  We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets from text prompts. Our model is directly trained on extensive noisy and unaligned in-the-wild' 3D assets. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation.
  arXiv  Detail & Related papers  (2024-06-06T17:58:15Z)
• Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion [18.67196713834323]
  This paper presents a novel approach to inpainting 3D regions of a scene, given masked multi-view images, by distilling a 2D diffusion model into a learned 3D scene representation (e.g. a NeRF) We show that this 2D diffusion model can still serve as a generative prior in a 3D multi-view reconstruction problem where we optimize a NeRF using a combination of score distillation sampling and NeRF reconstruction losses. Because our method can generate content to fill any 3D masked region, we additionally demonstrate 3D object completion, 3D object replacement, and 3D scene completion
  arXiv  Detail & Related papers  (2023-12-06T19:30:04Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection [31.58403386994297]
  We propose MonoNeRD, a novel detection framework that can infer dense 3D geometry and occupancy. Specifically, we model scenes with Signed Distance Functions (SDF), facilitating the production of dense 3D representations. To the best of our knowledge, this work is the first to introduce volume rendering for M3D, and demonstrates the potential of implicit reconstruction for image-based 3D perception.
  arXiv  Detail & Related papers  (2023-08-18T09:39:52Z)
• Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo [5.024813922014977]
  Retrieving the missing dimension information in acoustic images from 2D forward-looking sonar is a well-known problem in the field of underwater robotics. We propose a novel learning-based multi-view stereo method to estimate 3D information.
  arXiv  Detail & Related papers  (2022-07-30T14:35:21Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)
• A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis [163.96778522283967]
  We propose a shading-guided generative implicit model that is able to learn a starkly improved shape representation. An accurate 3D shape should also yield a realistic rendering under different lighting conditions. Our experiments on multiple datasets show that the proposed approach achieves photorealistic 3D-aware image synthesis.
  arXiv  Detail & Related papers  (2021-10-29T10:53:12Z)
• Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs [156.1209884183522]
  State-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image manifold. We present the first attempt to directly mine 3D geometric cues from an off-the-shelf 2D GAN that is trained on RGB images only.
  arXiv  Detail & Related papers  (2020-11-02T09:38:43Z)
• Improved Modeling of 3D Shapes with Multi-view Depth Maps [48.8309897766904]
  We present a general-purpose framework for modeling 3D shapes using CNNs. Using just a single depth image of the object, we can output a dense multi-view depth map representation of 3D objects.
  arXiv  Detail & Related papers  (2020-09-07T17:58:27Z)

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.