263 citations · 401 across the 15 of their papers we have counts for
18 papers
Broadband Hyperspectral 3D Imaging using Dispersed Structured Light
Suhyun Shin, Yunseong Moon, Ryota Maeda +3
Hyperspectral 3D imaging enables the capture of dense spectral information and scene geometry but has traditionally been confined to narrow spectral windows, typically the visible…
TC4D: Trajectory-Conditioned Text-to-4D Generation
Sherwin Bahmani, Xian Liu, Wang Yifan +9
Recent techniques for text-to-4D generation synthesize dynamic 3D scenes using supervision from pre-trained text-to-video models. However, existing representations for motion, such…
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Sherwin Bahmani, Ivan Skorokhodov, Victor Rong +7
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes. However, current text-to-4D methods face a t…
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction
Anagh Malik, Parsa Mirdehghan, Sotiris Nousias +2
Neural radiance fields (NeRFs) have become a ubiquitous tool for modeling scene appearance and geometry from multiview imagery. Recent work has also begun to explore how to use add…
MoSS: Monocular Shape Sensing for Continuum Robots
Chengnan Shentu, Enxu Li, Chaojun Chen +3
Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accura…
SparsePose: Sparse-View Camera Pose Regression and Refinement
Samarth Sinha, Jason Y. Zhang, Andrea Tagliasacchi +2
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. However, methods for pose estimation…
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler +9
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale…
Generative Neural Articulated Radiance Fields
Alexander W. Bergman, Petr Kellnhofer, Wang Yifan +3
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, h…
Residual Multiplicative Filter Networks for Multiscale Reconstruction
Shayan Shekarforoush, David B. Lindell, David J. Fleet +1
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 vo…
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
Qingqing Zhao, David B. Lindell, Gordon Wetzstein
Learned graph neural networks (GNNs) have recently been established as fast and accurate alternatives for principled solvers in simulating the dynamics of physical systems. In many…
3D GAN Inversion for Controllable Portrait Image Animation
Connor Z. Lin, David B. Lindell, Eric R. Chan +1
Millions of images of human faces are captured every single day; but these photographs portray the likeness of an individual with a fixed pose, expression, and appearance. Portrait…
BACON: Band-limited Coordinate Networks for Multiscale Scene Representation
David B. Lindell, Dave Van Veen, Jeong Joon Park +1
Coordinate-based networks have emerged as a powerful tool for 3D representation and scene reconstruction. These networks are trained to map continuous input coordinates to the valu…
ACORN: Adaptive Coordinate Networks for Neural Scene Representation
Julien N. P. Martel, David B. Lindell, Connor Z. Lin +3
Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as me…
AutoInt: Automatic Integration for Fast Neural Volume Rendering
David B. Lindell, Julien N. P. Martel, Gordon Wetzstein
Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering…
Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman +2
Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over con…
Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path
Christopher A. Metzler, David B. Lindell, Gordon Wetzstein
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 transie…
Sub-picosecond photon-efficient 3D imaging using single-photon sensors
Felix Heide, Steven Diamond, David B. Lindell +1
Active 3D imaging systems have broad applications across disciplines, including biological imaging, remote sensing and robotics. Applications in these domains require fast acquisit…
Non-line-of-sight Imaging with Partial Occluders and Surface Normals
Felix Heide, Matthew O'Toole, Kai Zang +3
Imaging objects obscured by occluders is a significant challenge for many applications. A camera that could "see around corners" could help improve navigation and mapping capabilit…