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Found 9 papers, 3 papers with code
Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension
Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions.
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction
The advantage of the multimodal model lies in its ability to process diverse sources of data using proper models and suitable fusion methods, which would enhance the noise resistance of the model while obtaining data diversity.
Density-invariant Features for Distant Point Cloud Registration
Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities.
Ranked #1 on
Point Cloud Registration
on nuScenes (Distant PCR)
APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction
Such features are then used for online distant point cloud registration.
Ranked #3 on
Point Cloud Registration
on nuScenes (Distant PCR)
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer
Mobile monocular 3D object detection (Mono3D) (e. g., on a vehicle, a drone, or a robot) is an important yet challenging task.
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation
To this end, we utilize a pose detection network to estimate the pose of the camera and then construct a feature map portraying pixel-level ground depth according to the 3D-to-2D perspective geometry.
DePS: An improved deep learning model for de novo peptide sequencing
De novo peptide sequencing from mass spectrometry data is an important method for protein identification.
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series
In this paper, we propose a light-weight semantic segmentation framework for large-scale point cloud series, called TempNet, which can improve both the accuracy and the stability of existing semantic segmentation models by combining a novel frame aggregation scheme.
COVID-19 Docking Server: A meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19
Motivation: The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019.
