A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection.

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem.

DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals-Planification, Implementation, and Preliminary Results.

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year.

Geometric Model and Calibration Method for a Solid-State LiDAR.

This paper presents a novel calibration method for solid-state LiDAR devices based on a geometrical description of their scanning system, which has variable angular resolution. Determining this distortion across the entire Field-of-View of the system yields accurate and precise measurements which enable it to be combined with other sensors. On the one hand, the geometrical model is formulated using the well-known Snell's law and the intrinsic optical assembly of the system, whereas on the other hand the proposed method describes the scanned scenario with an intuitive camera-like approach relating pixel locations with scanning directions.

Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network.

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network.

Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes.

Video segmentation is an important building block for high level applications such as scene understanding and interaction analysis. While outstanding results are achieved in this field by state-of-the-art learning and model based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provides actual world 3D geometry compared to 2D images.

Multi-view video representation based on fast Monte Carlo surface reconstruction.

This paper provides an alternative solution to the costly representation of multi-view video data, which can be used for both rendering and scene analyses. Initially, a new efficient Monte Carlo discrete surface reconstruction method for foreground objects with static background is presented, which outperforms volumetric techniques and is suitable for GPU environments. Some extensions are also presented, which allow a speeding up of the reconstruction by exploiting multi-resolution and temporal correlations.