Search Results for author: Yuan Ren

Found 13 papers, 0 papers with code

Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks

no code implementations11 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.


Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation: Technical Report for ICCV 2023 OmniObject3D Challenge

no code implementations28 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.


MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving

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.

3D Reconstruction Autonomous Driving

AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation

no code implementations2 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.

3D Object Detection Autonomous Vehicles +5

PCGen: Point Cloud Generator for LiDAR Simulation

no code implementations17 Oct 2022 Chenqi Li, Yuan Ren, Bingbing Liu

To tackle the first challenge, we propose FPA raycasting and surrogate model raydrop.

3D Reconstruction object-detection +1

A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation

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.

3D Object Detection Autonomous Driving +4

GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

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.

Panoptic Segmentation Segmentation

S3Net: 3D LiDAR Sparse Semantic Segmentation Network

no code implementations15 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.

Autonomous Driving LIDAR Semantic Segmentation +2

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

no code implementations16 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.

3D Semantic Scene Completion Segmentation +1

Analysis and Optimization of Service Delay for Multi-quality Videos in Multi-tier Heterogeneous Network with Random Caching

no code implementations21 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.

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