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Found 22 papers, 5 papers with code
3D Gaussian Splatting as Markov Chain Monte Carlo
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initialization.
Probing the 3D Awareness of Visual Foundation Models
Given that such models can classify, delineate, and localize objects in 2D, we ask whether they also represent their 3D structure?
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background.
NeRFiller: Completing Scenes via Generative 3D Inpainting
In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text.
Accelerating Neural Field Training via Soft Mining
We present an approach to accelerate Neural Field training by efficiently selecting sampling locations.
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation, which offers an alternative to off-the-shelf SfM pipelines which have well-understood failure modes.
Unsupervised Semantic Correspondence Using Stable Diffusion
Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images.
Ranked #1 on
Semantic correspondence
on CUB-200-2011
$\text{DC}^2$: Dual-Camera Defocus Control by Learning to Refocus
Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements.
ASIC: Aligning Sparse in-the-wild Image Collections
We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection.
Monocular Depth Estimation using Diffusion Models
To cope with the limited availability of data for supervised training, we leverage pre-training on self-supervised image-to-image translation tasks.
Ranked #20 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
DC2: Dual-Camera Defocus Control by Learning To Refocus
Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements.
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Our method works on in-the-wild online image collections of an object and produces relightable 3D assets for several use-cases such as AR/VR.
Learned Monocular Depth Priors in Visual-Inertial Initialization
Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR and autonomous robotic systems today, in both academia and industry.
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting
We present SLIDE, a modular and unified system for single image 3D photography that uses a simple yet effective soft layering strategy to better preserve appearance details in novel views.
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.
Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Unlike prior learning based work which has focused on predicting dense pixel-wise optical flow field and/or a depth map for each image, we propose to predict object instance specific 3D scene flow maps and instance masks from which we are able to derive the motion direction and speed for each object instance.
Learning a Multi-View Stereo Machine
We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches as well as recent learning based methods.
Shape and Symmetry Induction for 3D Objects
Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint.
Amodal Completion and Size Constancy in Natural Scenes
We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image.
Category-Specific Object Reconstruction from a Single Image
Object reconstruction from a single image -- in the wild -- is a problem where we can make progress and get meaningful results today.
Virtual View Networks for Object Reconstruction
All that structure from motion algorithms "see" are sets of 2D points.
