The recent advancements in neural radiance fields (NeRF) and related technologies have significantly pushed the boundaries of 3D scene representation and rendering. The field is currently witnessing a shift towards more efficient and scalable models, driven by the need for reduced computational complexity and improved rendering quality. Innovations such as content-aware quantization and training-free acceleration methods are paving the way for practical deployment in various applications. Additionally, the integration of neural fields into robotics is expanding their utility beyond computer vision, enhancing real-time applications and robot adaptability. Notably, the development of single-stage architectures for CAD sketch inference and the exploration of latent space editing for 3D objects are also contributing to the advancement of the field, offering new possibilities for design and manipulation. These developments collectively indicate a trend towards more efficient, scalable, and versatile solutions in 3D representation and rendering technologies.
Efficient and Scalable 3D Representation and Rendering
Sources
Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwidth Quantization
Evaluation of strategies for efficient rate-distortion NeRF streaming
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
Neural Fields in Robotics: A Survey
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting
Scaled Inverse Graphics: Efficiently Learning Large Sets of 3D Scenes
Manipulating Vehicle 3D Shapes through Latent Space Editing