Search Results for author: Yuexin Ma

Found 30 papers, 16 papers with code

LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors

no code implementations30 May 2022 Chengfeng Zhao, Yiming Ren, Yannan He, Peishan Cong, Han Liang, Jingyi Yu, Lan Xu, Yuexin Ma

We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using a single LiDAR and 4 IMUs.


STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

1 code implementation CVPR 2022 Peishan Cong, Xinge Zhu, Feng Qiao, Yiming Ren, Xidong Peng, Yuenan Hou, Lan Xu, Ruigang Yang, Dinesh Manocha, Yuexin Ma

In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes.

Pedestrian Detection

Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism

no code implementations20 Mar 2022 Yiming Ren, Peishan Cong, Xinge Zhu, Yuexin Ma

In this paper, we propose a self-supervised point cloud completion method (TraPCC) for vehicles in real traffic scenes without any complete data.

Point Cloud Completion

HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR

1 code implementation CVPR 2022 Yudi Dai, Yitai Lin, Chenglu Wen, Siqi Shen, Lan Xu, Jingyi Yu, Yuexin Ma, Cheng Wang

We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments.

3D Human Pose Estimation Autonomous Driving

NIMBLE: A Non-rigid Hand Model with Bones and Muscles

no code implementations9 Feb 2022 Yuwei Li, Longwen Zhang, Zesong Qiu, Yingwenqi Jiang, Nianyi Li, Yuexin Ma, Yuyao Zhang, Lan Xu, Jingyi Yu

Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world.

AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach

no code implementations9 Dec 2021 Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Yuexin Ma, Zhe Wang, Jianping Shi

Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline.

Domain Adaptation Stereo Matching

Referring Self-supervised Learning on 3D Point Cloud

no code implementations29 Sep 2021 Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Wenping Wang

In this paper, we study a new problem named Referring Self-supervised Learning (RSL) on 3D scene understanding: Given the 3D synthetic models with labels and the unlabeled 3D real scene scans, our goal is to distinguish the identical semantic objects on an unseen scene according to the referring synthetic 3D models.

Scene Understanding Self-Supervised Learning

SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation

1 code implementation22 Aug 2021 Xidong Peng, Xinge Zhu, Tai Wang, Yuexin Ma

Due to the information sparsity of local cost volume, we further introduce match reweighting and structure-aware attention, to make the depth information more concentrated.

Depth Estimation

Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection

1 code implementation21 Jul 2021 Runnan Chen, Yuexin Ma, Nenglun Chen, Lingjie Liu, Zhiming Cui, Yanhong Lin, Wenping Wang

Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.

Graph Attention

Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training

no code implementations28 May 2021 Runnan Chen, Yuexin Ma, Lingjie Liu, Nenglun Chen, Zhiming Cui, Guodong Wei, Wenping Wang

The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods.

SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos

1 code implementation23 Apr 2021 Xin Chen, Anqi Pang, Wei Yang, Yuexin Ma, Lan Xu, Jingyi Yu

In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.

Action Assessment Markerless Motion Capture

Input-Output Balanced Framework for Long-tailed LiDAR Semantic Segmentation

no code implementations26 Mar 2021 Peishan Cong, Xinge Zhu, Yuexin Ma

A thorough and holistic scene understanding is crucial for autonomous vehicles, where LiDAR semantic segmentation plays an indispensable role.

Autonomous Vehicles LIDAR Semantic Segmentation +2

ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References

1 code implementation CVPR 2021 Yannan He, Anqi Pang, Xin Chen, Han Liang, Minye Wu, Yuexin Ma, Lan Xu

We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner.

Category Disentangled Context: Turning Category-irrelevant Features Into Treasures

no code implementations1 Jan 2021 Keke Tang, Guodong Wei, Jie Zhu, Yuexin Ma, Runnan Chen, Zhaoquan Gu, Wenping Wang

Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features.

Computer Vision Image Classification

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

2 code implementations4 Aug 2020 Hui Zhou, Xinge Zhu, Xiao Song, Yuexin Ma, Zhe Wang, Hongsheng Li, Dahua Lin

A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space.

3D Semantic Segmentation LIDAR Semantic Segmentation

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

1 code implementation6 Apr 2020 Xinge Zhu, Yuexin Ma, Tai Wang, Yan Xu, Jianping Shi, Dahua Lin

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds.

3D Object Detection object-detection

Decision Propagation Networks for Image Classification

no code implementations27 Nov 2019 Keke Tang, Peng Song, Yuexin Ma, Zhaoquan Gu, Yu Su, Zhihong Tian, Wenping Wang

High-level (e. g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e. g., color) features in the early layers underexplored.

Classification General Classification +1

Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting

no code implementations10 Oct 2019 Runnan Chen, Yuexin Ma, Nenglun Chen, Daniel Lee, and Wenping Wang

Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.

Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting

2 code implementations23 Aug 2019 Runnan Chen, Yuexin Ma, Nenglun Chen, Daniel Lee, Wenping Wang

Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.

AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms

1 code implementation23 Jan 2019 Wei Li, Chengwei Pan, Rong Zhang, Jiaping Ren, Yuexin Ma, Jin Fang, Feilong Yan, Qichuan Geng, Xinyu Huang, Huajun Gong, Weiwei Xu, Guoping Wang, Dinesh Manocha, Ruigang Yang

Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.

Autonomous Driving

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

1 code implementation6 Nov 2018 Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).

Autonomous Vehicles Traffic Prediction +1

Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT

no code implementations7 Apr 2018 Yuexin Ma, Dinesh Manocha, Wenping Wang

We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes.

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