2 research outputs found
Semantic Stereo for Incidental Satellite Images
The increasingly common use of incidental satellite images for stereo
reconstruction versus rigidly tasked binocular or trinocular coincident
collection is helping to enable timely global-scale 3D mapping; however,
reliable stereo correspondence from multi-date image pairs remains very
challenging due to seasonal appearance differences and scene change. Promising
recent work suggests that semantic scene segmentation can provide a robust
regularizing prior for resolving ambiguities in stereo correspondence and
reconstruction problems. To enable research for pairwise semantic stereo and
multi-view semantic 3D reconstruction with incidental satellite images, we have
established a large-scale public dataset including multi-view, multi-band
satellite images and ground truth geometric and semantic labels for two large
cities. To demonstrate the complementary nature of the stereo and segmentation
tasks, we present lightweight public baselines adapted from recent state of the
art convolutional neural network models and assess their performance.Comment: Accepted publication at WACV 201
Photometric Multi-View Mesh Refinement for High-Resolution Satellite Images
Modern high-resolution satellite sensors collect optical imagery with ground
sampling distances (GSDs) of 30-50cm, which has sparked a renewed interest in
photogrammetric 3D surface reconstruction from satellite data. State-of-the-art
reconstruction methods typically generate 2.5D elevation data. Here, we present
an approach to recover full 3D surface meshes from multi-view satellite
imagery. The proposed method takes as input a coarse initial mesh and refines
it by iteratively updating all vertex positions to maximize the
photo-consistency between images. Photo-consistency is measured in image space,
by transferring texture from one image to another via the surface. We derive
the equations to propagate changes in texture similarity through the rational
function model (RFM), often also referred to as rational polynomial coefficient
(RPC) model. Furthermore, we devise a hierarchical scheme to optimize the
surface with gradient descent. In experiments with two different datasets, we
show that the refinement improves the initial digital elevation models (DEMs)
generated with conventional dense image matching. Moreover, we demonstrate that
our method is able to reconstruct true 3D geometry, such as facade structures,
if off-nadir views are available.Comment: Accepted for publication in ISPRS Journal of Photogrammetry and
Remote Sensin