Point Clouds

10 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

MVSNet: Depth Inference for Unstructured Multi-view Stereo

YoYo000/MVSNet ECCV 2018

We present an end-to-end deep learning architecture for depth map inference from multi-view images.

Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching

alibaba/cascade-stereo CVPR 2020

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.

Structure-From-Motion Revisited

colmap/colmap CVPR 2016

Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections.

Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness

touristCheng/UCSNet CVPR 2020

In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions.

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo


We propose a cost volume-based neural network for depth inference from multi-view images.

Visibility-aware Multi-view Stereo Network

jzhangbs/Vis-MVSNet 18 Aug 2020

As such, the adverse influence of occluded pixels is suppressed in the cost fusion.

PatchmatchNet: Learned Multi-View Patchmatch Stereo

FangjinhuaWang/PatchmatchNet CVPR 2021

We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo.

EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View Stereo

mindspore-ai/models ICCV 2021

As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark.

AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network

qt-zhu/aa-rmvsnet ICCV 2021

To overcome the difficulty of varying occlusion in complex scenes, we propose an inter-view cost volume aggregation module for adaptive pixel-wise view aggregation, which is able to preserve better-matched pairs among all views.

Curvature-guided dynamic scale networks for Multi-view Stereo

truongkhang/cds-mvsnet ICLR 2022

As a result, the method can process higher resolution inputs within faster run-time and lower memory than other MVS methods.