3,357 research outputs found
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification
Monocular Object Instance Segmentation and Depth Ordering with CNNs
In this paper we tackle the problem of instance-level segmentation and depth
ordering from a single monocular image. Towards this goal, we take advantage of
convolutional neural nets and train them to directly predict instance-level
segmentations where the instance ID encodes the depth ordering within image
patches. To provide a coherent single explanation of an image we develop a
Markov random field which takes as input the predictions of convolutional
neural nets applied at overlapping patches of different resolutions, as well as
the output of a connected component algorithm. It aims to predict accurate
instance-level segmentation and depth ordering. We demonstrate the
effectiveness of our approach on the challenging KITTI benchmark and show good
performance on both tasks.Comment: International Conference on Computer Vision (ICCV), 201
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
We present Im2Pano3D, a convolutional neural network that generates a dense
prediction of 3D structure and a probability distribution of semantic labels
for a full 360 panoramic view of an indoor scene when given only a partial
observation (<= 50%) in the form of an RGB-D image. To make this possible,
Im2Pano3D leverages strong contextual priors learned from large-scale synthetic
and real-world indoor scenes. To ease the prediction of 3D structure, we
propose to parameterize 3D surfaces with their plane equations and train the
model to predict these parameters directly. To provide meaningful training
supervision, we use multiple loss functions that consider both pixel level
accuracy and global context consistency. Experiments demon- strate that
Im2Pano3D is able to predict the semantics and 3D structure of the unobserved
scene with more than 56% pixel accuracy and less than 0.52m average distance
error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S
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