computer-vision-7

3D Point Cloud Reconstruction

3260 papers • 126 benchmarks • 313 datasets

Encoding and reconstruction of 3D point clouds.

(Image credit: Papersgraph)

Benchmarks

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Datasets

HUMAN4D

IBISCape

Boombox

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Most implemented papers

YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

Ahmad El Sallab, Waleed Ali, Sherif Abdelkarim, Mohamed Zahran, Mahmoud Zidan•Mon Aug 06 2018

The main contribution is in extending the loss function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian coordinates and the height of the box as a direct regression problem, which enables real-time performance, which is essential for automated driving.

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SampleNet: Differentiable Point Cloud Sampling

S. Avidan, Itai Lang, Asaf Manor•Sat Dec 07 2019

This work introduces a novel differentiable relaxation for point cloud sampling that approximates sampled points as a mixture of points in the primary input cloud and outperforms existing non-learned and learned sampling alternatives.

202 0

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3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image

Mayank Agarwal, R. Venkatesh Babu, Priyanka Mandikal, L. NavaneetK.•Thu Jul 19 2018

3D-LMNet, a latent embedding matching approach for 3D reconstruction, is proposed, which outperform state-of-the-art approaches on the task of single-view3D reconstruction on both real and synthetic datasets while generating multiple plausible reconstructions, demonstrating the generalizability and utility of the approach.

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3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image

R. Venkatesh Babu, Priyanka Mandikal, L. NavaneetK.•Fri Sep 07 2018

It is demonstrated that jointly training for both reconstruction and segmentation leads to improved performance in both the tasks, when compared to training for each task individually.

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CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision

Mayank Agarwal, R. Venkatesh Babu, Priyanka Mandikal, L. NavaneetK.•Tue Nov 27 2018

A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain 2D masks from a predicted 3D point cloud reconstruction, and significantly outperform the existing projection based approaches on a large-scale synthetic dataset.

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Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network

R. Venkatesh Babu, Priyanka Mandikal•Mon Dec 31 2018

This work introduces DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution, and proposes an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid.

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Real-time Scalable Dense Surfel Mapping

S. Shen, Kaixuan Wang, Fei Gao•Tue Apr 30 2019

A novel dense surfel mapping system that scales well in different environments with only CPU computation using a sparse SLAM system to estimate camera poses, which can fuse intensity images and depth images into a globally consistent model.

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Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation

Yu-Wing Tai, Hongwei Yi, Mingyu Ding, Runze Zhang, Zizhuang Wei, Yisong Chen, Guoping Wang•Thu Dec 05 2019

This paper proposes an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction and establishes a new state-of-the-art on the DTU dataset with significant improvements in the completeness and overall quality.

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Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes

Manmohan Chandraker, Zhengqin Li, Yu-Ying Yeh•Tue Apr 21 2020

This work proposes a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera, under a known but arbitrary environment map and renders a synthetic dataset to encourage the model to learn refractive light transport across different views.

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