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Found 6 papers, 5 papers with code
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics
Moreover, we propose a latent-property pairs acquisition method to effectively navigate the complexities of molecular latent optimization, a process that seems intuitive yet challenging due to the high-frequency and discontinuous nature of molecule space.
Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection
In previous work, an online density reduction is performed on the 4D Radar Tensor (4DRT) to reduce the data size, in which the density reduction level is chosen arbitrarily.
Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement
In addition, the second-stage network is found to be especially robust to lane occlusions, thus, demonstrating the robustness of the proposed network for driving in crowded environments.
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions
In this work, we introduce KAIST-Radar (K-Radar), a novel large-scale object detection dataset and benchmark that contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions, together with carefully annotated 3D bounding box labels of objects on the roads.
Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP).
Ranked #32 on
3D Point Cloud Classification
on ScanObjectNN
K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways
This is in contrast with Lidar lane detection networks (LLDNs), which can directly extract the lane lines on the bird's eye view (BEV) for motion planning and operate robustly under various lighting conditions.
Ranked #1 on
Lane Detection
on K-Lane
