Search Results for author:
Found 10 papers, 9 papers with code
One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving
Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips, costing huge computing resources.
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames.
Vision-Centric BEV Perception: A Survey
In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion.
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy.
Ranked #3 on
3D Object Detection
on nuScenes LiDAR only
Learning to Match Features with Seeded Graph Matching Network
2) Seeded Graph Neural Network, which utilizes seed matches to pass messages within/across images and predicts assignment costs.
VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
Experimental results validate the effectiveness of VMNet: specifically, on the challenging ScanNet dataset for large-scale segmentation of indoor scenes, it outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74. 6% vs 72. 5% and 73. 6% in mIoU) with a simpler network structure (17M vs 30M and 38M parameters).
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration.
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision.
Ranked #31 on
Semantic Segmentation
on S3DIS
ASLFeat: Learning Local Features of Accurate Shape and Localization
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors.
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point.
Ranked #2 on
Point Cloud Registration
on KITTI
