NeRFifyMesh：从纹理网格优化神经辐射场以构建机器人场景
NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene Building
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中文总结 AI 辅助
本文提出NeRFifyMesh流程，通过采样网格几何和纹理生成真实辐射场，将网格模型转换为NeRF，无需相机采样，渲染质量与基线相当，并应用于统一场景构建和碰撞仿真。
中文摘要 AI 辅助
在机器人学中，场景表示对于理解和与环境交互起着关键作用。神经辐射场（NeRF）及其变体的出现，作为一种新颖的表示方法，开辟了新的研究前沿。在语义映射和仿真等应用中，机器人专家旨在使用多个NeRF模型构建场景，每个模型代表一个物体。尽管已存在大量3D网格模型数据集，但迫切需要开发工具将这些资产转换为NeRF模型，以支持快速算法开发和测试。本文提出了一种新的流程，通过采样网格几何和纹理，人工生成基于点的真实辐射场，从而将现有网格模型转换为NeRF表示。该方法无需基于相机的采样或渲染原始网格的多视图图像来训练NeRF模型。广泛的基准测试表明，我们的方法在渲染质量上与基线方法相当。此外，通过构建统一的NeRF场景并利用提取的几何进行碰撞仿真，展示了该表示的应用。
英文摘要
In robotics, scene representation plays a pivotal role in understanding and interacting with the environment. The advent of Neural Radiance Fields (NeRF) and its variants, as a novel representation, has opened a new frontier of research. In applications such as semantic mapping and simulation, roboticists aim to build scenes using multiple NeRF models, each representing an object. While extensive datasets of 3D mesh models already exist, there is an urgent need to develop tools to convert these assets to NeRF models for rapid algorithm development and testing. This paper presents a new pipeline for converting existing mesh models to NeRF representations by artificially generating a ground truth point-based radiance field through sampling mesh geometry and texture. This approach alleviates the need for camera-based sampling or rendering multi-view images of the original mesh to train the NeRF model. Extensive benchmarking demonstrates that our method yields comparable rendering quality to the baselines. Additionally, the application of this representation is shown by constructing unified NeRF scenes and performing collision simulations with extracted geometry.
发表机构
- University of Toronto（多伦多大学）
- Toronto Metropolitan University（多伦多都会大学）
- University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。
