NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene Building
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.
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Key points
- 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.
Sources (1)
- [1]NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene BuildingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:45 PM
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.
In robotics, scene representation plays a pivotal role in understanding and interacting with the environment.
Extractive summary: sentences quoted from the sources.