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Found 20 papers, 7 papers with code
Disambiguating Monocular Depth Estimation with a Single Transient
Monocular depth estimation algorithms successfully predict the relative depth order of objects in a scene.
Flying with Photons: Rendering Novel Views of Propagating Light
Combined with this dataset, we introduce an efficient neural volume rendering framework based on the transient field.
TC4D: Trajectory-Conditioned Text-to-4D Generation
We learn local deformations that conform to the global trajectory using supervision from a text-to-video model.
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes.
Learning Lens Blur Fields
Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements.
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction
Here, we propose a novel method for rendering transient NeRFs that take as input the raw, time-resolved photon count histograms measured by a single-photon lidar system, and we seek to render such histograms from novel views.
MoSS: Monocular Shape Sensing for Continuum Robots
A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0. 91 mm, or 0. 36% of robot length) and real-time (70 fps) shape sensing on real-world data.
Passive Ultra-Wideband Single-Photon Imaging
We consider the problem of imaging a dynamic scene over an extreme range of timescales simultaneously--seconds to picoseconds--and doing so passively, without much light, and without any timing signals from the light source(s) emitting it.
SparsePose: Sparse-View Camera Pose Regression and Refinement
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene.
Generative Neural Articulated Radiance Fields
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress.
Residual Multiplicative Filter Networks for Multiscale Reconstruction
Coordinate networks like Multiplicative Filter Networks (MFNs) and BACON offer some control over the frequency spectrum used to represent continuous signals such as images or 3D volumes.
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
Given a sparse set of measurements, we are interested in recovering the initial condition or parameters of the PDE.
3D GAN Inversion for Controllable Portrait Image Animation
Portrait image animation enables the post-capture adjustment of these attributes from a single image while maintaining a photorealistic reconstruction of the subject's likeness or identity.
BACON: Band-limited Coordinate Networks for Multiscale Scene Representation
These networks are trained to map continuous input coordinates to the value of a signal at each point.
ACORN: Adaptive Coordinate Networks for Neural Scene Representation
Here, we introduce a new hybrid implicit-explicit network architecture and training strategy that adaptively allocates resources during training and inference based on the local complexity of a signal of interest.
AutoInt: Automatic Integration for Fast Neural Volume Rendering
For training, we instantiate the computational graph corresponding to the derivative of the network.
Implicit Neural Representations with Periodic Activation Functions
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.
Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path
Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transient, time-of-flight measurements.
Acoustic Non-Line-Of-Sight Imaging
Non-line-of-sight (NLOS) imaging enables unprecedented capabilities in a wide range of applications, including robotic and machine vision, remote sensing, autonomous vehicle navigation, and medical imaging.
Reconstructing Transient Images From Single-Photon Sensors
Computer vision algorithms build on 2D images or 3D videos that capture dynamic events at the millisecond time scale.
