Search Results for author:
Found 6 papers, 4 papers with code
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS
First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization.
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
At the same time, neural radiance fields have revolutionized novel view synthesis.
Learning Implicit Surface Light Fields
In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field.
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
In this work, we propose a differentiable rendering formulation for implicit shape and texture representations.
Texture Fields: Learning Texture Representations in Function Space
A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques.
Occupancy Networks: Learning 3D Reconstruction in Function Space
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.
