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Found 6 papers, 6 papers with code
RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding
Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions.
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
