Recently, DreamFusion demonstrated the utility of a pretrained text-to-image diffusion model
to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

Abstract Optimizing and rendering Neural Radiance Fields is computationally expensive
due to the vast number of samples required by volume rendering. Recent works have …

Text-to-image diffusion models are gradually introduced into computer graphics, recently
enabling the development of Text-to-3D pipelines in an open domain. However, for …

Abstract Implicit Neural Representations (INR) or neural fields have emerged as a popular
framework to encode multimedia signals such as images and radiance fields while retaining …

We propose a novel method that renders point clouds as if they are surfaces. The proposed
method is differentiable and requires no scene-specific optimization. This unique capability …

Neural graphics primitives are faster and achieve higher quality when their neural networks
are augmented by spatial data structures that hold trainable features arranged in a grid …

We introduce NeuralVDB, which improves on an existing industry standard for efficient
storage of sparse volumetric data, denoted VDB [Museth], by leveraging recent …

Abstract Since its proposal Neural Radiance Fields (NeRF) has achieved great success in
related tasks mainly adopting the hierarchical volume sampling (HVS) strategy for volume …

The common trade-offs of state-of-the-art methods for multi-shape representation (a single
model" packing" multiple objects) involve trading modeling accuracy against memory and …