Learning-based methods are demonstrating their strong competitiveness in estimating depth for multi-view stereo reconstruction in recent years. Among them the approaches that generate cost volumes based on the plane-sweeping algorithm and then use them for feature matching have shown to be very prominent recently. The plane-sweep volumes are essentially anisotropic in depth and spatial directions, but they are often approximated by isotropic cost volumes in those methods, which could be detrimental. In this paper, we propose a new end-to-end deep learning network of P-MVSNet for multi-view stereo based on isotropic and anisotropic 3D convolutions. Our P-MVSNet consists of two core modules: a patch-wise aggregation module learns to aggregate the pixel-wise correspondence information of extracted features to generate a matching confidence volume, from which a hybrid 3D U-Net then infers a depth probability distribution and predicts the depth maps. We perform extensive experiments on the DTU and Tanks & Temples benchmark datasets, and the results show that the proposed P-MVSNet achieves the state-of-the-art performance over many existing methods on multi-view stereo.

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P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo

Structure from Motion (SfM) is a pipeline that allows three-dimensional reconstruction starting from a collection of images. A typical SfM pipeline comprises different processing steps each of which tackles a different problem in the reconstruction pipeline. Each step can exploit different algorithms to solve the problem at hand and thus many different SfM pipelines can be built. How to choose the SfM pipeline best suited for a given task is an important question. In this paper we report a comparison of different state-of-the-art SfM pipelines in terms of their ability to reconstruct different scenes. We also propose an evaluation procedure that stresses the SfM pipelines using real dataset acquired with high-end devices as well as realistic synthetic dataset. To this end, we created a plug-in module for the Blender software to support the creation of synthetic datasets and the evaluation of the SfM pipeline. The use of synthetic data allows us to easily have arbitrarily large and diverse datasets with, in theory, infinitely precise ground truth. Our evaluation procedure considers both the reconstruction errors as well as the estimation errors of the camera poses used in the reconstruction.

Evaluating the Performance of Structure from Motion Pipelines

. The paper reports some comparisons between commercial software able to automatically process image datasets for 3D reconstruction purposes. The main aspects investigated in the work are the capability to correctly orient large sets of image of complex environments, the metric quality of the results, replicability and redundancy. Different datasets are employed, each one featuring a diverse number of images, GSDs at cm and mm resolutions, and ground truth information to perform statistical analyses of the 3D results. A summary of (photogrammetric) terms is also provided, in order to provide rigorous terms of reference for comparisons and critical analyses.

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A critical review of automated photogrammetricprocessing of large datasets

We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo. With high computation speed and low memory requirement, PatchmatchNet can process higher resolution imagery and is more suited to run on resource limited devices than competitors that employ 3D cost volume regularization. For the first time we introduce an iterative multi-scale Patchmatch in an end-to-end trainable architecture and improve the Patchmatch core algorithm with a novel and learned adaptive propagation and evaluation scheme for each iteration. Extensive experiments show a very competitive performance and generalization for our method on DTU, Tanks & Temples and ETH3D, but at a significantly higher efficiency than all existing top-performing models: at least two and a half times faster than state-of-the-art methods with twice less memory usage.

PatchmatchNet: Learned Multi-View Patchmatch Stereo

Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress

. During the last two decades we have witnessed great improvements in ICT hardware and software technologies. Three-dimensional content is starting to become commonplace now in many applications. Although for many years 3D technologies have been used in the generation of assets by researchers and experts, nowadays these tools are starting to become commercially available to every citizen. This is especially the case for smartphones, that are powerful enough and sufficiently widespread to perform a huge variety of activities (e.g. paying, calling, communication, photography, navigation, localization, etc.), including just very recently the possibility of running 3D reconstruction pipelines. The REPLICATE project is tackling this particular issue, and it has an ambitious vision to enable ubiquitous 3D creativity via the development of tools for mobile 3D-assets generation on smartphones/tablets. This article presents the REPLICATE project’s concept and some of the ongoing activities, with particular attention being paid to advances made in the first year of work. Thus the article focuses on the system architecture definition, selection of optimal frames for 3D cloud reconstruction, automated generation of sparse and dense point clouds, mesh modelling techniques and post-processing actions. Experiments so far were concentrated on indoor objects and some simple heritage artefacts, however, in the long term we will be targeting a larger variety of scenarios and communities.

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A smartphone-based 3d pipeline for the creative industry– the replicate eu project

This work proposes a progressive patch based multiview stereo algorithm able to deliver a dense point cloud at any time. This enables an immediate feedback on the reconstruction process in a user centric scenario. With increasing processing time, the model is improved in terms of resolution and accuracy. The algorithm explicitly handles input images with varying effective scale and creates visually pleasing point clouds. A priority scheme assures that the limited computational power is invested in scene parts, where the user is most interested in or the overall error can be reduced the most. The architecture of the proposed pipeline allows fast processing times in large scenes using a pure open-source CPU implementation. We show the performance of our algorithm on challenging standard datasets as well as on real-world scenes and compare it to the baseline.

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Progressive Prioritized Multi-view Stereo

This article presents a pipeline that enables multiple users to collaboratively acquire images with monocular smartphones and derive a 3D point cloud using a remote reconstruction server. A set of key images are automatically selected from each smartphone's camera video feed as multiple users record different viewpoints of an object, concurrently or at different time instants. Selected images are automatically processed and registered with an incremental Structure from Motion (SfM) algorithm in order to create a 3D model. Our incremental SfM approach enables on-the-fly feedback to the user to be generated about current reconstruction progress. Feedback is provided in the form of a preview window showing the current 3D point cloud, enabling users to see if parts of a surveyed scene need further attention/coverage whilst they are still in situ. We evaluate our 3D reconstruction pipeline by performing experiments in uncontrolled and unconstrained real-world scenarios. Datasets are publicly available.

Cloud-based collaborative 3D reconstruction using smartphones

. The paper presents a collaborative image-based 3D reconstruction pipeline to perform image acquisition with a smartphone and geometric 3D reconstruction on a server during concurrent or disjoint acquisition sessions. Images are selected from the video feed of the smartphone’s camera based on their quality and novelty. The smartphone’s app provides on-the-fly reconstruction feedback to users co-involved in the acquisitions. The server is composed of an incremental SfM algorithm that processes the received images by seamlessly merging them into a single sparse point cloud using bundle adjustment. Dense image matching algorithm can be lunched to derive denser point clouds. The reconstruction details, experiments and performance evaluation are presented and discussed.

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3d reconstruction with a collaborative approachbased on smartphones and a cloud-based server

Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art.

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Alex Locher

Papers

A smartphone-based 3d pipeline for the creative industry– the replicate eu project

Progressive Prioritized Multi-view Stereo

Cloud-based collaborative 3D reconstruction using smartphones

3d reconstruction with a collaborative approachbased on smartphones and a cloud-based server

Progressive Structure from Motion