Pixel2point in PyTorch
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Updated
Feb 13, 2023 - Python
Pixel2point in PyTorch
Neural Reflectance Field from Shading and Shadow under a Fixed Viewpoint
A new method to preprocess ShapeNet to get minimal shift 3D ground truth; 3 Stage single-view 3D reconstruction method; Point cloud surface reconstruction without input normals.
[ECCV 2024] SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models
Exploring the types of losses and decoder functions for regressing to voxels, point clouds, and mesh representations from single view RGB input.
GAMesh: Guided and Augmented Meshing for Deep Point Networks
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
Modular framework for experimenting with single-view voxel reconstruction, combined with domain adaptation.
Python implementation of Single View Metrology using numpy and opencv
[AAAI 2023] Official implementation of "Occupancy Planes for Single-view RGB-D Human Reconstruction"
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction (CVPR 2022)
Implementation of CVPR'23: Learning 3D Scene Priors with 2D Supervision
Code and datasets for TPAMI 2021 "SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images "
[CVPR 2024] Official implementation of Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
[CVPR 2024] EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion
A diffuser implementation of Zero123. Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV23)
(ECCV 2022) Code for Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
Metrical Monocular Photometric Tracker [ECCV2022]
We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
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