102 research outputs found
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
Child population, economic development and regional inequality of education resources in China
There is great inequality of educational resources between different provinces in China due to unbalanced economic development. Despite continued redistribution of financial resources by the central government in favor of poorer provinces, educational inequality remains. In this paper, we argue that focusing on educational resources is far from sufficient. Poorer provinces do not only suffer from a lower level of educational resources, but they also have more children to educate, i.e. a greater need for education. Combining and analyzing the data in the Sixth National Population Census of China and the official statistics on education spending and resources, we found that provincial-level variations in the child population and the child dependency ratio have made access to educational resources even more unequal given the unequal financial capacity at the provincial level. Poorer provinces face a higher child dependency ratio and have lower economic development, and these two factors jointly lead to limited educational resources. Apart from a much higher level of redistribution in favor of less developed provinces, encouraging more balanced distribution of teachers and more broadly promoting economic equality are essential to reduce inequality in educational resources in China
EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps
Accurate and reliable ego-localization is critical for autonomous driving. In
this paper, we present EgoVM, an end-to-end localization network that achieves
comparable localization accuracy to prior state-of-the-art methods, but uses
lightweight vectorized maps instead of heavy point-based maps. To begin with,
we extract BEV features from online multi-view images and LiDAR point cloud.
Then, we employ a set of learnable semantic embeddings to encode the semantic
types of map elements and supervise them with semantic segmentation, to make
their feature representation consistent with BEV features. After that, we feed
map queries, composed of learnable semantic embeddings and coordinates of map
elements, into a transformer decoder to perform cross-modality matching with
BEV features. Finally, we adopt a robust histogram-based pose solver to
estimate the optimal pose by searching exhaustively over candidate poses. We
comprehensively validate the effectiveness of our method using both the
nuScenes dataset and a newly collected dataset. The experimental results show
that our method achieves centimeter-level localization accuracy, and
outperforms existing methods using vectorized maps by a large margin.
Furthermore, our model has been extensively tested in a large fleet of
autonomous vehicles under various challenging urban scenes.Comment: 8 page
A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation
Pairwise point cloud registration is a critical task for many applications,
which heavily depends on finding correct correspondences from the two point
clouds. However, the low overlap between input point clouds causes the
registration to fail easily, leading to mistaken overlapping and mismatched
correspondences, especially in scenes where non-overlapping regions contain
similar structures. In this paper, we present a unified bird's-eye view (BEV)
model for jointly learning of 3D local features and overlap estimation to
fulfill pairwise registration and loop closure. Feature description is
performed by a sparse UNet-like network based on BEV representation, and 3D
keypoints are extracted by a detection head for 2D locations, and a regression
head for heights. For overlap detection, a cross-attention module is applied
for interacting contextual information of input point clouds, followed by a
classification head to estimate the overlapping region. We evaluate our unified
model extensively on the KITTI dataset and Apollo-SouthBay dataset. The
experiments demonstrate that our method significantly outperforms existing
methods on overlap estimation, especially in scenes with small overlaps. It
also achieves top registration performance on both datasets in terms of
translation and rotation errors.Comment: 8 pages. Accepted by ICRA-202
Downregulation of SPARC expression decreases gastric cancer cellular invasion and survival
<p>Abstract</p> <p>Background</p> <p>Secreted protein acidic and rich in cysteine (SPARC) plays a key role in the development of many tissues and organ types. Aberrant SPARC expression was found in a wide variety of human cancers, contributes to tumor development. Because SPARC was found to be overexpressed in human gastric cancer tissue, we therefore to explore the expression of SPARC in gastric cancer lines and the carcinogenic mechanisms.</p> <p>Methods</p> <p>SPARC expression was evaluated in a panel of human gastric cancer cell lines. MGC803 and HGC 27 gastric cancer cell lines expressing high level of SPARC were transiently transfected with SPARC-specific small interfering RNAs and subsequently evaluated for effects on invasion and proliferation.</p> <p>Results</p> <p>Small interfering RNA-mediated knockdown of SPARC in MGC803 and HGC 27 gastric cancer cells dramatically decreased their invasion. Knockdown of SPARC was also observed to significantly increase the apoptosis of MGC803 and HGC 27 gastric cancer cells compared with control transfected group.</p> <p>Conclusions</p> <p>Our data showed that downregulating of SPARC inhibits invasion and growth of human gastric cancer cells. Thus, targeting of SPARC could be an effective therapeutic approach against gastric cancer.</p
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