Object Reconstruction
78 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Object Reconstruction
A Large Dataset of Object Scans
HOWS
Latest papers
NTO3D: Neural Target Object 3D Reconstruction with Segment Anything
NTO3D lifts the 2D masks and features of SAM into the 3D neural field for high-quality neural target object 3D reconstruction.
Sparse 3D Reconstruction via Object-Centric Ray Sampling
This sampling scheme relies on the mesh representation to ensure also that samples are well-distributed along its normals.
ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
qianyiwu/objectsdf_plus
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ICCV 2023
Unlike traditional multi-view stereo approaches, the neural implicit surface-based methods leverage neural networks to represent 3D scenes as signed distance functions (SDFs).
A One Stop 3D Target Reconstruction and multilevel Segmentation Method
ganlab/ostra
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We extend object tracking and 3D reconstruction algorithms to support continuous segmentation labels to leverage the advances in the 2D image segmentation, especially the Segment-Anything Model (SAM) which uses the pretrained neural network without additional training for new scenes, for 3D object segmentation.
Contact-conditioned hand-held object reconstruction from single-view images
2023-MindSpore-4/Code12
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journal 2023
Reconstructing the shape of hand-held objects from single-view color images is a long-standing problem in computer vision and computer graphics.
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
sail-sg/numcc
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NeurIPS 2023
Second, our Repulsive UDF is a novel alternative to the occupancy field used in MCC, significantly improving the quality of 3D object reconstruction.
CAD-Estate: Large-scale CAD Model Annotation in RGB Videos
google-research/cad-estate
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ICCV 2023
We propose a method for annotating videos of complex multi-object scenes with a globally-consistent 3D representation of the objects.
gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction
In particular, we address reconstruction of hands and manipulated objects from monocular RGB images.
Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization
To address this problem, we propose a neural implicit surface learning method called Depth-NeuS based on depth information optimization for multi-view reconstruction.
RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction
Recently, neural implicit surfaces have become popular for multi-view reconstruction.
