NeRFifyMesh: テクスチャ付きメッシュからロボティクスシーン構築のためのNeRFを最適化
NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene Building
既存の3DメッシュモデルからNeRF表現を生成する新しいパイプラインを提案し、ロボティクスシーン構築や衝突シミュレーションに応用した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Nillan Nimal, Mahboubeh Asadi, Sajad Saeedi
分類: cs.RO
原文アブストラクト
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.
