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Found 28 papers, 13 papers with code
SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians
3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering.
Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes
Our key idea is to leverage powerful video diffusion models as the generative component of our model and to combine these with a robust technique to lift 2D videos into meaningful 3D motion.
G2SDF: Surface Reconstruction from Explicit Gaussians with Implicit SDFs
By establishing a differentiable connection between the explicit Gaussians and the implicit SDF, our approach enables high-quality surface reconstruction and rendering.
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.
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Our OpenNeRF further leverages NeRF's ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images.
Recent Trends in 3D Reconstruction of General Non-Rigid Scenes
Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision.
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.
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes.
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
In this work, we propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections.
DNS SLAM: Dense Neural Semantic-Informed SLAM
In this work, we introduce DNS SLAM, a novel neural RGB-D semantic SLAM approach featuring a hybrid representation.
TextMesh: Generation of Realistic 3D Meshes From Text Prompts
In addition, we propose a novel way to finetune the mesh texture, removing the effect of high saturation and improving the details of the output 3D mesh.
NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM
However, in real-time and on-the-fly scene capture applications, this prior knowledge cannot be assumed as fixed or static, since it dynamically changes and it is subject to significant updates based on run-time observations.
DreamBooth3D: Subject-Driven Text-to-3D Generation
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject.
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields.
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction.
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.
Shape As Points: A Differentiable Poisson Solver
However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
At test time, our model generates images with explicit control over the camera as well as the shape and appearance of the scene.
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional.
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity.
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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.
Convolutional Occupancy Networks
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.
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.
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
In order to perform dense 4D reconstruction from images or sparse point clouds, we combine our method with a continuous 3D representation.
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.
