Search Results for author:
Found 16 papers, 2 papers with code
Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation
The increasing availability of digital 3D environments, whether through image-based 3D reconstruction, generation, or scans obtained by robots, is driving innovation across various applications.
FlowR: Flowing from Sparse to Dense 3D Reconstructions
3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates.
Fillerbuster: Multi-View Scene Completion for Casual Captures
Our solution is to train a generative model that can consume a large context of input frames while generating unknown target views and recovering image poses when desired.
Coherent 3D Scene Diffusion From a Single RGB Image
Motivated by the ill-posed nature of the task and to obtain consistent scene reconstruction results, we learn a generative scene prior by conditioning on all scene objects simultaneously to capture the scene context and by allowing the model to learn inter-object relationships throughout the diffusion process.
Multi-view Image Diffusion via Coordinate Noise and Fourier Attention
Recently, text-to-image generation with diffusion models has made significant advancements in both higher fidelity and generalization capabilities compared to previous baselines.
L3DG: Latent 3D Gaussian Diffusion
We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation.
MultiDiff: Consistent Novel View Synthesis from a Single Image
We introduce MultiDiff, a novel approach for consistent novel view synthesis of scenes from a single RGB image.
ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing
This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency, thus enabling high-fidelity instruction-guided scene editing.
ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models
In this paper, we present a method that leverages pretrained text-to-image models as a prior, and learn to generate multi-view images in a single denoising process from real-world data.
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions.
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes.
Panoptic Lifting for 3D Scene Understanding with Neural Fields
We propose Panoptic Lifting, a novel approach for learning panoptic 3D volumetric representations from images of in-the-wild scenes.
DiffRF: Rendering-Guided 3D Radiance Field Diffusion
We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models.
3D Multi-Object Tracking with Differentiable Pose Estimation
We propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments.
AutoRF: Learning 3D Object Radiance Fields from Single View Observations
We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view.
Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences
From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space.
