600 research outputs found
Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds
Urban modeling from LiDAR point clouds is an important topic in computer
vision, computer graphics, photogrammetry and remote sensing. 3D city models
have found a wide range of applications in smart cities, autonomous navigation,
urban planning and mapping etc. However, existing datasets for 3D modeling
mainly focus on common objects such as furniture or cars. Lack of building
datasets has become a major obstacle for applying deep learning technology to
specific domains such as urban modeling. In this paper, we present a
urban-scale dataset consisting of more than 160 thousands buildings along with
corresponding point clouds, mesh and wire-frame models, covering 16 cities in
Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art
algorithms including handcrafted and deep feature based methods. Experimental
results indicate that Building3D has challenges of high intra-class variance,
data imbalance and large-scale noises. The Building3D is the first and largest
urban-scale building modeling benchmark, allowing a comparison of supervised
and self-supervised learning methods. We believe that our Building3D will
facilitate future research on urban modeling, aerial path planning, mesh
simplification, and semantic/part segmentation etc
Vision technology/algorithms for space robotics applications
The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed
Volumetric Wireframe Parsing from Neural Attraction Fields
The primal sketch is a fundamental representation in Marr's vision theory,
which allows for parsimonious image-level processing from 2D to 2.5D
perception. This paper takes a further step by computing 3D primal sketch of
wireframes from a set of images with known camera poses, in which we take the
2D wireframes in multi-view images as the basis to compute 3D wireframes in a
volumetric rendering formulation. In our method, we first propose a NEural
Attraction (NEAT) Fields that parameterizes the 3D line segments with
coordinate Multi-Layer Perceptrons (MLPs), enabling us to learn the 3D line
segments from 2D observation without incurring any explicit feature
correspondences across views. We then present a novel Global Junction
Perceiving (GJP) module to perceive meaningful 3D junctions from the NEAT
Fields of 3D line segments by optimizing a randomly initialized
high-dimensional latent array and a lightweight decoding MLP. Benefitting from
our explicit modeling of 3D junctions, we finally compute the primal sketch of
3D wireframes by attracting the queried 3D line segments to the 3D junctions,
significantly simplifying the computation paradigm of 3D wireframe parsing. In
experiments, we evaluate our approach on the DTU and BlendedMVS datasets with
promising performance obtained. As far as we know, our method is the first
approach to achieve high-fidelity 3D wireframe parsing without requiring
explicit matching.Comment: Technical report; Video can be found at https://youtu.be/qtBQYbOpVp
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