10 research outputs found
Automatic Registration of RGBD Scans via Salient Directions
We address the problem of wide-baseline registration of
RGB-D data, such as photo-textured laser scans without
any artificial targets or prediction on the relative motion.
Our approach allows to fully automatically register scans
taken in GPS-denied environments such as urban canyon,
industrial facilities or even indoors. We build upon image
features which are plenty, localized well and much more
discriminative than geometry features; however, they suffer
from viewpoint distortions and request for normalization.
We utilize the principle of salient directions present in
the geometry and propose to extract (several) directions
from the distribution of surface normals or other cues such
as observable symmetries. Compared to previous work we
pose no requirements on the scanned scene (like containing
large textured planes) and can handle arbitrary surface
shapes. Rendering the whole scene from these repeatable
directions using an orthographic camera generates textures
which are identical up to 2D similarity transformations.
This ambiguity is naturally handled by 2D features and allows
to find stable correspondences among scans. For geometric
pose estimation from tentative matches we propose a
fast and robust 2 point sample consensus scheme integrating
an early rejection phase. We evaluate our approach on
different challenging real world scenes
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and
multi-view reconstruction pipelines. The problem is that viewing angle and
distance severely impact the recognizability of a local feature. Attempts to
improve appearance invariance by choosing better local feature points or by
leveraging outside information, have come with pre-requisites that made some of
them impractical. In this paper, we propose a surprisingly effective
enhancement to local feature extraction, which improves matching. We show that
CNN-based depths inferred from single RGB images are quite helpful, despite
their flaws. They allow us to pre-warp images and rectify perspective
distortions, to significantly enhance SIFT and BRISK features, enabling more
good matches, even when cameras are looking at the same scene but in opposite
directions.Comment: 14 pages, 7 figures, accepted for publication at the European
conference on computer vision (ECCV) 202
Point Cloud Library: Three-Dimensional Object Recognition and 6DOF Pose Estimation
With the advent of new-generation depth sensors, the use of three-dimensional (3-D) data is becoming increasingly popular. As these sensors are commodity hardware and sold at low cost, a rapidly growing group of people can acquire 3- D data cheaply and in real time
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions