MONOCULAR DEPTH PREDICTION IN PHOTOGRAMMETRIC APPLICATIONS

Abstract. Despite the recent success of learning-based monocular depth estimation algorithms and the release of large-scale datasets for training, the methods are limited to depth map prediction and still struggle to yield reliable results in the 3D space without additional scene cues. Indeed, although state-of-the-art approaches produce quality depth maps, they generally fail to recover the 3D structure of the scene robustly. This work explores supervised CNN architectures for monocular depth estimation and evaluates their potential in 3D reconstruction. Since most available datasets for training are not designed toward this goal and are limited to specific indoor scenarios, a new metric, large-scale synthetic benchmark (ArchDepth) is introduced that renders near real-world scenarios of outdoor scenes. A encoder-decoder architecture is used for training, and the generalization of the approach is evaluated via depth inference in unseen views in synthetic and real-world scenarios. The depth map predictions are also projected in the 3D space using a separate module. Results are qualitatively and quantitatively evaluated and compared with state-of-the-art algorithms for single image 3D scene recovery

Heterogeneity of reported outcomes in epidermolysis bullosa clinical research:a scoping review as a first step towards outcome harmonization

BACKGROUND: Epidermolysis bullosa (EB) is a rare, genetically and clinically heterogeneous group of skin fragility disorders. No cure is currently available, but many novel and repurposed treatments are upcoming. For adequate evaluation and comparison of clinical studies in EB, well-defined and consistent consensus-endorsed outcomes and outcome measurement instruments are necessary.OBJECTIVES: To identify previously reported outcomes in EB clinical research, group these outcomes by outcome domains and areas and summarize respective outcome measurement instruments.METHODS: A systematic literature search was performed in the databases MEDLINE, Embase, Scopus, Cochrane CENTRAL, CINAHL, PsycINFO and trial registries covering the period between January 1991 and September 2021. Studies were included if they evaluated a treatment in a minimum of three patients with EB. Two reviewers independently performed the study selection and data extraction. All identified outcomes and their respective instruments were mapped onto overarching outcome domains. The outcome domains were stratified according to subgroups of EB type, age group, intervention, decade and phase of clinical trial.RESULTS: The included studies (n = 207) covered a range of study designs and geographical settings. A total of 1280 outcomes were extracted verbatim and inductively mapped onto 80 outcome domains and 14 outcome areas. We found a steady increase in the number of published clinical trials and outcomes reported over the past 30 years. The included studies mainly focused on recessive dystrophic EB (43%). Wound healing was reported most frequently across all studies and referred to as a primary outcome in 31% of trials. Great heterogeneity of reported outcomes was observed within all stratified subgroups. Moreover, a diverse range of outcome measurement instruments (n = 200) was identified.CONCLUSIONS: We show substantial heterogeneity in reported outcomes and outcome measurement instruments in EB clinical research over the past 30 years. This review is the first step towards harmonization of outcomes in EB, which is necessary to expedite the clinical translation of novel treatments for patients with EB.</p

Monocular depth prediction in photogrammetric applications

Despite the recent success of learning-based monocular depth estimation algorithms and the release of large-scale datasets for training, the methods are limited to depth map prediction and still struggle to yield reliable results in the 3D space without additional scene cues. Indeed, although state-of-the-art approaches produce quality depth maps, they generally fail to recover the 3D structure of the scene robustly. This work explores supervised CNN architectures for monocular depth estimation and evaluates their potential in 3D reconstruction. Since most available datasets for training are not designed toward this goal and are limited to specific indoor scenarios, a new metric, large-scale synthetic benchmark (ArchDepth) is introduced that renders near real-world scenarios of outdoor scenes. A encoder-decoder architecture is used for training, and the generalization of the approach is evaluated via depth inference in unseen views in synthetic and real-world scenarios. The depth map predictions are also projected in the 3D space using a separate module. Results are qualitatively and quantitatively evaluated and compared with state-of-the-art algorithms for single image 3D scene recovery

Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds

A big challenge in geodata processing is the seamless and accurate integration of airborne LiDAR (Light Detection And Ranging) and photogrammetric point clouds performed by properly considering their high variations in resolution and precision. In this paper we propose a new approach to co-register airborne point clouds acquired by LiDAR sensors and photogrammetric algorithms, assuming that only dense point clouds from both mapping methods are available, without LiDAR raw data nor flight trajectories. First, semantically segmented point clouds are quality-wise evaluated by assigning sensor-specific quality features to each 3D point. Then, these quality features are aggregated in order to assign a score to each 3D point based on its quality. Finally, using a voxel-based structure, a filtering step is performed to select only the best points used for the registration refinement. We assess the performance of the proposed method on two different case studies to demonstrate its advantages compared to a traditional ICP-based approach. The code of the implemented method is available at https://github.com/3DOM-FBK/HyRe