Search Results for author:
Found 10 papers, 4 papers with code
IPOD: Intensive Point-based Object Detector for Point Cloud
We present a novel 3D object detection framework, named IPOD, based on raw point cloud.
Ranked #1 on
3D Object Detection
on KITTI Pedestrians Easy
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD).
3DSSD: Point-based 3D Single Stage Object Detector
Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
3D-MAN: 3D Multi-frame Attention Network for Object Detection
In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset.
A Unified Query-based Paradigm for Point Cloud Understanding
This is achieved by introducing an intermediate representation, i. e., Q-representation, in the querying stage to serve as a bridge between the embedding stage and task heads.
Ranked #7 on
Semantic Segmentation
on S3DIS
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding
Masked signal modeling has greatly advanced self-supervised pre-training for language and 2D images.
Fully Sparse 3D Occupancy Prediction
Occupancy prediction plays a pivotal role in autonomous driving.
Visual Point Cloud Forecasting enables Scalable Autonomous Driving
To resolve this, we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input.
Improving Distant 3D Object Detection Using 2D Box Supervision
This mapping allows the depth estimation of distant objects conditioned on their 2D boxes, making long-range 3D detection with 2D supervision feasible.
CN: Channel Normalization For Point Cloud Recognition
In 3D recognition, to fuse multi-scale structure information, existing methods apply hierarchical frameworks stacked by multiple fusion layers for integrating current relative locations with structure information from the previous level.
