activity
20172026
most citedVoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

318 citations · 799 across the 25 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CV2023

Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving

Mahyar Najibi, Jingwei Ji, Yin Zhou +4

Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new objec…

cs.CV2023★ 2 cited

3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels

Zhenzhen Weng, Alexander S. Gorban, Jingwei Ji +3

Training a 3D human keypoint detector from point clouds in a supervised manner requires large volumes of high quality labels. While it is relatively easy to capture large amounts o…

cs.CV2023

MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences

Yingwei Li, Charles R. Qi, Yin Zhou +2

Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded…

cs.RO2023★ 1 cited

MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion

Chiyu Max Jiang, Andre Cornman, Cheolho Park +3

We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages:…

cs.CV2023★ 2 cited

GINA-3D: Learning to Generate Implicit Neural Assets in the Wild

Bokui Shen, Xinchen Yan, Charles R. Qi +5

Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.…