no code implementations • 11 Feb 2024 • Muqun Niu, Yuan Ren, Boyu Li, Chenchen Ding
Lightweight design of Convolutional Neural Networks (CNNs) requires co-design efforts in the model architectures and compression techniques.
no code implementations • 28 Sep 2023 • Zheyuan Yang, Yibo Liu, Guile Wu, Tongtong Cao, Yuan Ren, Yang Liu, Bingbing Liu
To resolve this problem, we study learning effective NeRFs and SDFs representations with 3D Generative Adversarial Networks (GANs) for 3D object generation.
no code implementations • ICCV 2023 • Yibo Liu, Kelly Zhu, Guile Wu, Yuan Ren, Bingbing Liu, Yang Liu, Jinjun Shan
This set-level latent code is an expression of the optimal 3D shape in the implicit space, and can be subsequently decoded to a continuous SDF of the vehicle.
no code implementations • 2 Feb 2023 • YiXuan Xu, Hamidreza Fazlali, Yuan Ren, Bingbing Liu
In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud.
no code implementations • ICCV 2023 • Guile Wu, Tongtong Cao, Bingbing Liu, Xingxin Chen, Yuan Ren
In this work, we propose the first attempt to explore multi-domain learning and generalization for LiDAR-based 3D object detection.
no code implementations • 17 Oct 2022 • Chenqi Li, Yuan Ren, Bingbing Liu
To tackle the first challenge, we propose FPA raycasting and surrogate model raydrop.
no code implementations • CVPR 2022 • Hamidreza Fazlali, YiXuan Xu, Yuan Ren, Bingbing Liu
In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone.
no code implementations • ICCV 2021 • Ryan Razani, Ran Cheng, Enxu Li, Ehsan Taghavi, Yuan Ren, Liu Bingbing
GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information.
no code implementations • 15 Mar 2021 • Ran Cheng, Ryan Razani, Yuan Ren, Liu Bingbing
In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches.
no code implementations • 16 Dec 2020 • Ran Cheng, Christopher Agia, Yuan Ren, Xinhai Li, Liu Bingbing
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike.
Ranked #2 on 3D Semantic Scene Completion on SemanticKITTI
no code implementations • 21 Jul 2020 • Xuewei Zhang, Tiejun Lv, Yuan Ren, Wei Ni, Norman C. Beaulieu
Aiming to minimize service delay, we propose a new random caching scheme in device-to-device (D2D)-assisted heterogeneous network.