In this paper, we introduce a comprehensive benchmark for learning-based monocular depth estimation methods. In recent years, depthmap calculation methods have achieved impressive results. The process of comparing the performance of different models requires a lot of resources and time, which can be costly for many developers. The goal of our benchmark is to provide a comparison of how the top models perform on various datasets. We present a table with results based on our tests performed on several popular datasets and one dataset introduced in this paper. In addition, we provide a toolkit that allows each model selected for the benchmark to be tested on any suitable dataset.

Topological and node noise filtration are typically considered separately. Graph Neural Networks (GNN) are commonly used for node noise filtration, as they offer high efficiency and low exploitation costs. This paper explores the solution of joint node and topological noise filtration through the use of graph neural networks. Since treating a 3D mesh as a graph is challenging, an indicator function grid representation is employed as input for GNNs to perform the joint filtering. The resulting machine learning model is inspired by point cloud to mesh reconstruction algorithms and demonstrates low computational requirements during inference, producing successful results for smooth, watertight 3D models.

In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Addressing this deficiency, this paper introduces a novel benchmarking methodology to facilitate a fair and robust comparison of RecSys algorithms, thereby advancing evaluation practices. By utilizing a diverse set of 30 open datasets, including two introduced in this work, and evaluating 11 collaborative filtering algorithms across 9 metrics, we critically examine the influence of dataset characteristics on algorithm performance. We further investigate the feasibility of aggregating outcomes from multiple datasets into a unified ranking. Through rigorous experimental analysis, we validate the reliability of our methodology under the variability of datasets, offering a benchmarking strategy that balances quality and computational demands. This methodology enables a fair yet effective means of evaluating RecSys algorithms, providing valuable guidance for future research endeavors.