Search Results for author:
Found 12 papers, 6 papers with code
Lagrangian Hashing for Compressed Neural Field Representations
We present Lagrangian Hashing, a representation for neural fields combining the characteristics of fast training NeRF methods that rely on Eulerian grids (i. e.~InstantNGP), with those that employ points equipped with features as a way to represent information (e. g. 3D Gaussian Splatting or PointNeRF).
Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displays
Uniquely, a single inference step of our model supports different permutations of these perceptual tasks at different prompted rates (i. e., mildly, lightly), eliminating the need for daisy-chaining multiple models to get the desired perceptual effect.
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering.
Compact Neural Graphics Primitives with Learned Hash Probing
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.
ATT3D: Amortized Text-to-3D Object Synthesis
Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields.
Magic3D: High-Resolution Text-to-3D Content Creation
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
Ranked #2 on
Text to 3D
on T$^3$Bench
Variable Bitrate Neural Fields
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations.
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 x 1600 pixels).
Ranked #1 on
Novel View Synthesis
on RTMV
A Dataset and Explorer for 3D Signed Distance Functions
Reference datasets are a key tool in the creation of new algorithms.
Neural Fields in Visual Computing and Beyond
Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time.
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes
We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality.
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Here, we propose a new two-stream CNN architecture for semantic segmentation that explicitly wires shape information as a separate processing branch, i. e. shape stream, that processes information in parallel to the classical stream.
Ranked #24 on
Semantic Segmentation
on Cityscapes test
