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Found 17 papers, 6 papers with code
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes.
NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer
Visual Place Recognition (VPR) is crucial in computer vision, aiming to retrieve database images similar to a query image from an extensive collection of known images.
Cross Domain Early Crop Mapping using CropSTGAN
Notably, CropSTGAN significantly outperforms these methods in scenarios with large data distribution dissimilarities between the target and source domains.
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured.
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images.
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces
The first component is to borrow the tri-plane representation from EG3D and represent signed distance fields as a mixture of tri-planes and MLPs instead of representing it with MLPs only.
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
We then propose to alternately optimize the implicit function and the registration between the implicit function and point cloud.
Learning Harmonic Molecular Representations on Riemannian Manifold
In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes
3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world.
CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
In this paper, we propose a new method called CroSel, which leverages historical predictions from the model to identify true labels for most training examples.
DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis
To mine the information in probability volume, we creatively synthesize the source depths by splattering the probability volume and depth hypotheses to source views.
EpiGRAF: Rethinking training of 3D GANs
In this work, we show that it is possible to obtain a high-resolution 3D generator with SotA image quality by following a completely different route of simply training the model patch-wise.
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
We develop HF-NeuS, a novel method to improve the quality of surface reconstruction in neural rendering.
DepthGAN: GAN-based Depth Generation of Indoor Scenes from Semantic Layouts
Limited by the computational efficiency and accuracy, generating complex 3D scenes remains a challenging problem for existing generation networks.
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation
Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years.
Ranked #5 on
Depth Estimation
on Stanford2D3D Panoramic
MGCN: Descriptor Learning using Multiscale GCNs
Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.
A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes With Incompatible Shape Structures
Focusing on this issue, in this paper, we present a more discriminative local descriptor for deformable 3D shapes with incompatible structures.
