Scaling NeRF for Multiple Scenes

• Exploring 3D-aware Latent Spaces presents a new method enabling NeRFs to efficiently learn numerous semantically-similar scenes.

• Combines learning 3D-aware latent spaces with Tri-Plane scene representations to reduce training time and memory per scene.

• Shows remarkable reductions in per-scene costs and demonstrates scalability when training 1,000 scenes.

• Key Findings:

  • Reduces memory costs by 44% and time costs by 86% for per-scene training.
  • Shares common information across scenes, reducing model complexity.

• Opinions and Further Research:

  • This work signifies a remarkable leap in the practical application of NeRF, enabling scalable learning across multiple scenes while optimizing resource usage. It opens prospects for widespread adoption in fields such as virtual reality, film production, and digital heritage preservation. Future research may delve into fine-tuning the scalability and efficiency aspects, potentially revolutionizing how we interact with 3D environments.