computer-vision-5

3D Object Reconstruction From A Single Image

3260 papers • 126 benchmarks • 313 datasets

Image: Fan et al

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Most implemented papers

Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

Heng Li•Fri Mar 15 2013

BWA-MEM automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment, which is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases.

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

Hao Su, L. Guibas, Haoqiang Fan•Thu Dec 01 2016

This paper addresses the problem of 3D reconstruction from a single image, generating a straight-forward form of output unorthordox, and designs architecture, loss function and learning paradigm that are novel and effective, capable of predicting multiple plausible 3D point clouds from an input image.

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

T. Simon, Jason M. Saragih, H. Joo, Shunsuke Saito•Tue Mar 31 2020

This work formulates a multi-level architecture that is end-to-end trainable and significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images.

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

Minh Vo, S. Narasimhan, Dinesh Reddy Narapureddy•Fri May 31 2019

We present Occlusion-Net, a framework to predict 2D and 3D locations of occluded keypoints for objects, in a largely self-supervised manner. We use an off-the-shelf detector as input (like MaskRCNN) that is trained only on visible key point annotations. This is the only supervision used in this work. A graph encoder network then explicitly classifies invisible edges and a graph decoder network corrects the occluded keypoint locations from the initial detector. 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. The 2D keypoints are then passed into a 3D graph network that estimates the 3D shape and camera pose using the self-supervised re-projection loss. At test time, our approach successfully localizes keypoints in a single view under a diverse set of severe occlusion settings. We demonstrate and evaluate our approach on synthetic CAD data as well as a large image set capturing vehicles at many busy city intersections. As an interesting aside, we compare the accuracy of human labels of invisible keypoints against those obtained from geometric trifocal-tensor loss.

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Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics

T. Brox, Wolfram Burgard, Maxim Tatarchenko, Oier Mees•Wed Oct 16 2019

A convolutional neural network for joint 3D shape prediction and viewpoint estimation from a single input image that gets the learning signal from a silhouette of an object in the input image-a form of self-supervision.

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ARCH: Animatable Reconstruction of Clothed Humans

Christoph Lassner, Tony Tung, Zeng Huang, Yuanlu Xu, Hao Li•Tue Apr 07 2020

This paper proposes ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image and shows numerous qualitative examples of animated, high-quality reconstructed avatars unseen in the literature so far.

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From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks

R. Venkatesh Babu, V. Jampani, Wei-Chih Hung, K. L. Navaneet, Ansu Mathew, Shashank Kashyap•Mon May 04 2020

A key novelty of the proposed technique is to impose 3D geometric reasoning into predicted 3D point clouds by rotating them with randomly sampled poses and then enforcing cycle consistency on both 3D reconstructions and poses.

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

S. Lucey, Chen-Hsuan Lin, Chaoyang Wang•Mon Oct 19 2020

This paper derives a novel differentiable rendering formulation for learning signed distance functions (SDF) from 2D silhouettes and proposes SDF-SRN, an approach that outperforms the state of the art under challenging single-view supervision settings on both synthetic and real-world datasets.

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Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency

Alexei A. Efros, Matthew Fisher, Mathieu Aubry, Tom Monnier•Wed Apr 20 2022

The main contributions are two ways for leveraging cross-instance consistency: progressive conditioning, a training strategy to gradually specialize the model from category to instances in a curriculum learning fashion; and neighbor reconstruction, a loss enforcing consistency between instances having similar shape or texture.

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