5,463 research outputs found
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
We propose a new multi-frame method for efficiently computing scene flow
(dense depth and optical flow) and camera ego-motion for a dynamic scene
observed from a moving stereo camera rig. Our technique also segments out
moving objects from the rigid scene. In our method, we first estimate the
disparity map and the 6-DOF camera motion using stereo matching and visual
odometry. We then identify regions inconsistent with the estimated camera
motion and compute per-pixel optical flow only at these regions. This flow
proposal is fused with the camera motion-based flow proposal using fusion moves
to obtain the final optical flow and motion segmentation. This unified
framework benefits all four tasks - stereo, optical flow, visual odometry and
motion segmentation leading to overall higher accuracy and efficiency. Our
method is currently ranked third on the KITTI 2015 scene flow benchmark.
Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3
orders of magnitude faster than the top six methods. We also report a thorough
evaluation on challenging Sintel sequences with fast camera and object motion,
where our method consistently outperforms OSF [Menze and Geiger, 2015], which
is currently ranked second on the KITTI benchmark.Comment: 15 pages. To appear at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017). Our results were submitted to KITTI 2015 Stereo
Scene Flow Benchmark in November 201
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
This paper presents a robotic pick-and-place system that is capable of
grasping and recognizing both known and novel objects in cluttered
environments. The key new feature of the system is that it handles a wide range
of object categories without needing any task-specific training data for novel
objects. To achieve this, it first uses a category-agnostic affordance
prediction algorithm to select and execute among four different grasping
primitive behaviors. It then recognizes picked objects with a cross-domain
image classification framework that matches observed images to product images.
Since product images are readily available for a wide range of objects (e.g.,
from the web), the system works out-of-the-box for novel objects without
requiring any additional training data. Exhaustive experimental results
demonstrate that our multi-affordance grasping achieves high success rates for
a wide variety of objects in clutter, and our recognition algorithm achieves
high accuracy for both known and novel grasped objects. The approach was part
of the MIT-Princeton Team system that took 1st place in the stowing task at the
2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
available online at http://arc.cs.princeton.eduComment: Project webpage: http://arc.cs.princeton.edu Summary video:
https://youtu.be/6fG7zwGfIk
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