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Found 8 papers, 4 papers with code
Evolutive Rendering Models
Central to this framework is the development of differentiable versions of these rendering elements, allowing for effective gradient backpropagation from the final rendering objectives.
Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians
In response, we propose the first RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes all benefits of globally optimized tracking by adapting dynamically to keyframe pose and depth updates by actively deforming the 3D Gaussian map.
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.
