Context-aware Human Motion Prediction

The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision. Current state-of-the-art formulate this problem as a sequence-to-sequence task, in which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that predicts future movements, typically in the order of 1 to 2 seconds. However, one aspect that has been obviated so far, is the fact that human motion is inherently driven by interactions with objects and/or other humans in the environment. In this paper, we explore this scenario using a novel context-aware motion prediction architecture. We use a semantic-graph model where the nodes parameterize the human and objects in the scene and the edges their mutual interactions. These interactions are iteratively learned through a graph attention layer, fed with the past observations, which now include both object and human body motions. Once this semantic graph is learned, we inject it to a standard RNN to predict future movements of the human/s and object/s. We consider two variants of our architecture, either freezing the contextual interactions in the future of updating them. A thorough evaluation in the "Whole-Body Human Motion Database" shows that in both cases, our context-aware networks clearly outperform baselines in which the context information is not considered.Comment: Accepted at CVPR2

Mapping for Indoor Walking Environment from Point Clouds by Using Mobile Mapping Systems

Walkability is one of the issues to be addressed in the planning of smart urban cities. Although, there is a substantial amount of studies on outdoor walking pedestrian, limited study has been done to address indoor walkability. Recently, most of the pedestrians are likely to use indoor route than outdoor route to protect themselves from sun and rain as most of the indoor routes are located on the buildings such as shopping mall and rail transit station. Therefore, it important to collect all the relevant information in the indoor building to addressed the walkability issues. The GeoSLAM ZEB REVO scanner is used for its convenience to access narrow space, busy area and complex building structure. This scanner is portable and easy to handle by the operator as it can be attached on the cart or carry it with backpack. The scanner captures the building geometry and facilities and present it in the form of point cloud. Then necessary information can be extracted from the point cloud using point cloud segmentation method. The end user such as town planner can benefit from the final product to design future building with pedestrian-friendly tool to encourage more people to walk. Therefore, it brings impact to the society by providing the healthy lifestyle in addition to reducing the use of private vehicle on the road

Point Completion Networks and Segmentation of 3D Mesh

Deep learning has made many advancements in fields such as computer vision, natural language processing and speech processing. In autonomous driving, deep learning has made great improvements pertaining to the tasks of lane detection, steering estimation, throttle control, depth estimation, 2D and 3D object detection, object segmentation and object tracking. Understanding the 3D world is necessary for safe end-to-end self-driving. 3D point clouds provide rich 3D information, but processing point clouds is difficult since point clouds are irregular and unordered. Neural point processing methods like GraphCNN and PointNet operate on individual points for accurate classification and segmentation results. Occlusion of these 3D point clouds remains a major problem for autonomous driving. To process occluded point clouds, this research explores deep learning models to fill in missing points from partial point clouds. Specifically, we introduce improvements to methods called deep multistage point completion networks. We propose novel encoder and decoder architectures for efficiently processing partial point clouds as input and outputting complete point clouds. Results will be demonstrated on ShapeNet dataset. Deep learning has made significant advancements in the field of robotics. For a robot gripper such as a suction cup to hold an object firmly, the robot needs to determine which portions of an object, or specifically which surfaces of the object should be used to mount the suction cup. Since 3D objects can be represented in many forms for computational purposes, a proper representation of 3D objects is necessary to tackle this problem. Formulating this problem using deep learning problem provides dataset challenges. In this work we will show representing 3D objects in the form of 3D mesh is effective for the problem of a robot gripper. We will perform research on the proper way for dataset creation and performance evaluation

Dense 3D point cloud reconstruction using a deep pyramid network

Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs. © 2019 IEEE