3d-shape-reconstruction

3D Shape Reconstruction From A Single 2D Image

3260 papers • 126 benchmarks • 313 datasets

Image: Liao et al

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-shape-reconstruction

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Dataset

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Actions

ApolloCar3D

Libraries

i

Use these libraries to find 3d-shape-reconstruction models and implementations

Datasets

ApolloCar3D

Subtasks

Shape from Texture

Most implemented papers

Multi-Garment Net: Learning to Dress 3D People From Images

C. Theobalt, Gerard Pons-Moll, Bharat Lal Bhatnagar, Garvita Tiwari•Sun Aug 18 2019

Multi-Garment Network is presented, a method to predict body shape and clothing, layered on top of the SMPL model from a few frames of a video, allowing to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses.

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ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization

Z. Kira, Rares Ambrus, Sergey Zakharov, Adrien Gaidon, Muhammad Zubair Irshad, T. Kollar•Fri Dec 31 2021

This work presents ShAPO, a method for joint multi-object detection, 3D textured reconstruction, 6D object pose and size estimation, which significantly out-performs all baselines on the NOCS dataset with an 8% absolute improvement in mAP for 6D pose estimation.

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PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

Angjoo Kanazawa, S. Morishima, Shunsuke Saito, Zeng Huang, Ryota Natsume•Sun May 12 2019

The proposed Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object, achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

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GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision

Yu-Wing Tai, Chi-Keung Tang, Lei Ke, Yanan Sun, Shichao Li•Sat Jul 25 2020

A novel end-to-end framework named GSNet (Geometric and Scene-aware Network), which jointly estimates 6DoF poses and reconstructs detailed 3D car shapes from single urban street view, and inspires a new multi-objective loss function to regularize network training.

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Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

Angjoo Kanazawa, Deva Ramanan, J. Malik, H. Joo, Jason Y. Zhang, Sam Pepose•Wed Jul 29 2020

A method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment is presented.

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ROCA: Robust CAD Model Retrieval and Alignment from a Single Image

Angela Dai, M. Nießner, Can Gümeli•Thu Dec 02 2021

Experiments on challenging, real-world imagery from ScanNet show that ROCA signif-icantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy.

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Super-resolution 3D Human Shape from a Single Low-Resolution Image

A. Hilton, M. Volino, Marco Pesavento•Mon Aug 22 2022

Extensive experiments show that the proposed approach can estimate super-resolution human geometries with a significantly higher level of detail than that obtained with previous approaches when applied to low-resolution images.

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