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PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

CVPR 2020 facebookresearch/pifuhd

Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.

3D HUMAN POSE ESTIMATION 3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D SHAPE RECONSTRUCTION

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

ICCV 2019 shunsukesaito/PIFu

We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.

3D HUMAN POSE ESTIMATION 3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D SHAPE RECONSTRUCTION FROM A SINGLE 2D IMAGE

A Point Set Generation Network for 3D Object Reconstruction from a Single Image

CVPR 2017 fanhqme/PointSetGeneration

Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.

Ranked #2 on 3D Reconstruction on Data3D−R2N2 (using extra training data)

3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D RECONSTRUCTION

Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

CVPR 2019 dineshreddy91/Occlusion_Net

Central to this work is a trifocal tensor loss that provides indirect self-supervision for occluded keypoint locations that are visible in other views of the object.

3D CAR INSTANCE UNDERSTANDING 3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D POSE ESTIMATION VEHICLE POSE ESTIMATION

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images

NeurIPS 2020 chenhsuanlin/signed-distance-SRN

Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets.

3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D RECONSTRUCTION

From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks

CVPR 2020 val-iisc/ssl_3d_recon

We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision.

3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D POINT CLOUD RECONSTRUCTION POSE ESTIMATION