10,133 research outputs found
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
To better address challenging issues of the irregularity and inhomogeneity
inherently present in 3D point clouds, researchers have been shifting their
focus from the design of hand-craft point feature towards the learning of 3D
point signatures using deep neural networks for 3D point cloud classification.
Recent proposed deep learning based point cloud classification methods either
apply 2D CNN on projected feature images or apply 1D convolutional layers
directly on raw point sets. These methods cannot adequately recognize
fine-grained local structures caused by the uneven density distribution of the
point cloud data. In this paper, to address this challenging issue, we
introduced a density-aware convolution module which uses the point-wise density
to re-weight the learnable weights of convolution kernels. The proposed
convolution module is able to fully approximate the 3D continuous convolution
on unevenly distributed 3D point sets. Based on this convolution module, we
further developed a multi-scale fully convolutional neural network with
downsampling and upsampling blocks to enable hierarchical point feature
learning. In addition, to regularize the global semantic context, we
implemented a context encoding module to predict a global context encoding and
formulated a context encoding regularizer to enforce the predicted context
encoding to be aligned with the ground truth one. The overall network can be
trained in an end-to-end fashion with the raw 3D coordinates as well as the
height above ground as inputs. Experiments on the International Society for
Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated
the superiority of the proposed method for point cloud classification. Our
model achieved a new state-of-the-art performance with an average F1 score of
71.2% and improved the performance by a large margin on several categories
PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation
The development of powerful 3D scanning hardware and reconstruction
algorithms has strongly promoted the generation of 3D surface reconstructions
in different domains. An area of special interest for such 3D reconstructions
is the cultural heritage domain, where surface reconstructions are generated to
digitally preserve historical artifacts. While reconstruction quality nowadays
is sufficient in many cases, the robust analysis (e.g. segmentation, matching,
and classification) of reconstructed 3D data is still an open topic. In this
paper, we target the automatic and interactive segmentation of high-resolution
3D surface reconstructions from the archaeological domain. To foster research
in this field, we introduce a fully annotated and publicly available
large-scale 3D surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset to the community. We provide baseline
results for our existing random forest-based approach and for the first time
investigate segmentation with convolutional neural networks (CNNs) on the data.
Results show that both approaches have complementary strengths and weaknesses
and that the provided dataset represents a challenge for future research.Comment: CBMI Submission; Dataset and more information can be found at
http://lrs.icg.tugraz.at/research/petroglyphsegmentation
SEGCloud: Semantic Segmentation of 3D Point Clouds
3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision
(3DV 2017
Pointwise Convolutional Neural Networks
Deep learning with 3D data such as reconstructed point clouds and CAD models
has received great research interests recently. However, the capability of
using point clouds with convolutional neural network has been so far not fully
explored. In this paper, we present a convolutional neural network for semantic
segmentation and object recognition with 3D point clouds. At the core of our
network is pointwise convolution, a new convolution operator that can be
applied at each point of a point cloud. Our fully convolutional network design,
while being surprisingly simple to implement, can yield competitive accuracy in
both semantic segmentation and object recognition task.Comment: 10 pages, 6 figures, 10 tables. Paper accepted to CVPR 201
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