6,274 research outputs found
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
J-MOD: Joint Monocular Obstacle Detection and Depth Estimation
In this work, we propose an end-to-end deep architecture that jointly learns
to detect obstacles and estimate their depth for MAV flight applications. Most
of the existing approaches either rely on Visual SLAM systems or on depth
estimation models to build 3D maps and detect obstacles. However, for the task
of avoiding obstacles this level of complexity is not required. Recent works
have proposed multi task architectures to both perform scene understanding and
depth estimation. We follow their track and propose a specific architecture to
jointly estimate depth and obstacles, without the need to compute a global map,
but maintaining compatibility with a global SLAM system if needed. The network
architecture is devised to exploit the joint information of the obstacle
detection task, that produces more reliable bounding boxes, with the depth
estimation one, increasing the robustness of both to scenario changes. We call
this architecture J-MOD. We test the effectiveness of our approach with
experiments on sequences with different appearance and focal lengths and
compare it to SotA multi task methods that jointly perform semantic
segmentation and depth estimation. In addition, we show the integration in a
full system using a set of simulated navigation experiments where a MAV
explores an unknown scenario and plans safe trajectories by using our detection
model
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