3D Shape Reconstruction
59 papers with code • 2 benchmarks • 8 datasets
Pix3D
Hypersim
ApolloCar3D
4DFAB
HUMAN4D
FewSOL
Foot3D
3D-POP
Latest papers with no code
DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields
Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction.
A Review of Deep Learning-Powered Mesh Reconstruction Methods
With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life.
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
We instead propose to decompose shapes using a library of 3D parts provided by the user, giving full control over the choice of parts.
MiShape: 3D Shape Modelling of Mitochondria in Microscopy
Fluorescence microscopy is a quintessential tool for observing cells and understanding the underlying mechanisms of life-sustaining processes of all living organisms.
3D Colored Shape Reconstruction from a Single RGB Image through Diffusion
In shape prediction module, the reference RGB image is first encoded into a high-level shape feature and then the shape feature is utilized as a condition to predict the reverse geometric noise in diffusion model.
Teleidoscopic Imaging System for Microscale 3D Shape Reconstruction
The planar mirrors virtually define multiple viewpoints by multiple reflections, and the monocentric lens realizes a high magnification with less blurry and surround view even in closeup imaging.
Neural Poisson: Indicator Functions for Neural Fields
Implicit neural field generating signed distance field representations (SDFs) of 3D shapes have shown remarkable progress in 3D shape reconstruction and generation.
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning
This method cuts a part of a 3D shape in each step which is then approximated as a polygon mesh.
Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)
Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays.
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns.
