Search Results for author: Yuexin Ma

Found 74 papers, 32 papers with code

Part2Object: Hierarchical Unsupervised 3D Instance Segmentation

1 code implementation14 Jul 2024 Cheng Shi, Yulin Zhang, Bin Yang, Jiajin Tang, Yuexin Ma, Sibei Yang

By training Hi-Mask3D on the objects and object parts extracted from Part2Object, we achieve consistent and superior performance compared to state-of-the-art models in various settings, including unsupervised instance segmentation, data-efficient fine-tuning, and cross-dataset generalization.

3D Instance Segmentation Clustering +4

LiveHPS++: Robust and Coherent Motion Capture in Dynamic Free Environment

no code implementations13 Jul 2024 Yiming Ren, Xiao Han, Yichen Yao, Xiaoxiao Long, Yujing Sun, Yuexin Ma

LiDAR-based human motion capture has garnered significant interest in recent years for its practicability in large-scale and unconstrained environments.

ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios

no code implementations7 May 2024 Dingrui Wang, Zheyuan Lai, Yuda Li, Yi Wu, Yuexin Ma, Johannes Betz, Ruigang Yang, Wei Li

Furthermore, a new metric named clamped temporal error (CTE) is proposed to give a more comprehensive evaluation of prediction performance, especially in time-sensitive emergency events of subseconds.

Autonomous Driving

Multi-Space Alignments Towards Universal LiDAR Segmentation

1 code implementation CVPR 2024 Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, Yuexin Ma

A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception.

Autonomous Driving Diversity +1

A Unified Framework for Human-centric Point Cloud Video Understanding

no code implementations CVPR 2024 Yiteng Xu, Kecheng Ye, Xiao Han, Yiming Ren, Xinge Zhu, Yuexin Ma

Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds, further advancing downstream human-centric tasks and applications.

3D Pose Estimation Action Recognition +4

RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method

no code implementations CVPR 2024 Ming Yan, Yan Zhang, Shuqiang Cai, Shuqi Fan, Xincheng Lin, Yudi Dai, Siqi Shen, Chenglu Wen, Lan Xu, Yuexin Ma, Cheng Wang

Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes.

Gaze-guided Hand-Object Interaction Synthesis: Benchmark and Method

no code implementations24 Mar 2024 Jie Tian, Lingxiao Yang, Ran Ji, Yuexin Ma, Lan Xu, Jingyi Yu, Ye Shi, Jingya Wang

Here, the object motion diffusion model generates sequences of object motions based on gaze conditions, while the hand motion diffusion model produces hand motions based on the generated object motion.

Denoising Human motion prediction +2

LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment

1 code implementation20 Mar 2024 Peishan Cong, Ziyi Wang, Zhiyang Dou, Yiming Ren, Wei Yin, Kai Cheng, Yujing Sun, Xiaoxiao Long, Xinge Zhu, Yuexin Ma

Language-guided scene-aware human motion generation has great significance for entertainment and robotics.

GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image

no code implementations18 Mar 2024 Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, Xiaoxiao Long

We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes, e. g., depth and normals, from single images.

3D Reconstruction Diversity

LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment

no code implementations CVPR 2024 Yiming Ren, Xiao Han, Chengfeng Zhao, Jingya Wang, Lan Xu, Jingyi Yu, Yuexin Ma

For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications.

Scene Understanding

GaussianPro: 3D Gaussian Splatting with Progressive Propagation

no code implementations22 Feb 2024 Kai Cheng, Xiaoxiao Long, Kaizhi Yang, Yao Yao, Wei Yin, Yuexin Ma, Wenping Wang, Xuejin Chen

The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed.

Neural Rendering Patch Matching

RealDex: Towards Human-like Grasping for Robotic Dexterous Hand

no code implementations21 Feb 2024 Yumeng Liu, Yaxun Yang, Youzhuo Wang, Xiaofei Wu, Jiamin Wang, Yichen Yao, Sören Schwertfeger, Sibei Yang, Wenping Wang, Jingyi Yu, Xuming He, Yuexin Ma

In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data.

Extreme Two-View Geometry From Object Poses with Diffusion Models

1 code implementation5 Feb 2024 Yujing Sun, Caiyi Sun, YuAn Liu, Yuexin Ma, Siu Ming Yiu

Human has an incredible ability to effortlessly perceive the viewpoint difference between two images containing the same object, even when the viewpoint change is astonishingly vast with no co-visible regions in the images.

Camera Pose Estimation Object +1

HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations

1 code implementation30 Dec 2023 Yilan Dong, Chunlin Yu, Ruiyang Ha, Ye Shi, Yuexin Ma, Lan Xu, Yanwei Fu, Jingya Wang

Existing gait recognition benchmarks mostly include minor clothing variations in the laboratory environments, but lack persistent changes in appearance over time and space.

Gait Recognition

OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries

1 code implementation6 Dec 2023 Yuhang Lu, Xinge Zhu, Tai Wang, Yuexin Ma

Occupancy prediction has increasingly garnered attention in recent years for its fine-grained understanding of 3D scenes.

GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces

no code implementations CVPR 2024 Yingwenqi Jiang, Jiadong Tu, YuAn Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang, Yuexin Ma

In this paper, we present GaussianShader, a novel method that applies a simplified shading function on 3D Gaussians to enhance the neural rendering in scenes with reflective surfaces while preserving the training and rendering efficiency.

Neural Rendering

UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving

no code implementations28 Nov 2023 Kai Cheng, Xiaoxiao Long, Wei Yin, Jin Wang, Zhiqiang Wu, Yuexin Ma, Kaixuan Wang, Xiaozhi Chen, Xuejin Chen

Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities.

Autonomous Driving Depth Estimation +1

Wonder3D: Single Image to 3D using Cross-Domain Diffusion

1 code implementation CVPR 2024 Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, YuAn Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, Wenping Wang

In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry.

Image to 3D Single-View 3D Reconstruction

Learning to Adapt SAM for Segmenting Cross-domain Point Clouds

no code implementations13 Oct 2023 Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Tai Wang, Xinge Zhu, Yuexin Ma

Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.

General Knowledge Image Segmentation +4

Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training

no code implementations29 Sep 2023 Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Tongliang Liu, Wenping Wang

In this paper, we propose Model2Scene, a novel paradigm that learns free 3D scene representation from Computer-Aided Design (CAD) models and languages.

3D Semantic Segmentation Object

Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation

no code implementations19 Sep 2023 Jingyu Zhang, Huitong Yang, Dai-Jie Wu, Jacky Keung, Xuesong Li, Xinge Zhu, Yuexin Ma

Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations.

Domain Adaptation Semantic Segmentation +1

Human-centric Scene Understanding for 3D Large-scale Scenarios

1 code implementation ICCV 2023 Yiteng Xu, Peishan Cong, Yichen Yao, Runnan Chen, Yuenan Hou, Xinge Zhu, Xuming He, Jingyi Yu, Yuexin Ma

Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human-environment interactions, severe occlusions in crowds, etc.

Action Recognition Scene Understanding +1

ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds

no code implementations25 Apr 2023 Xiangze Jia, Hui Zhou, Xinge Zhu, Yandong Guo, Ji Zhang, Yuexin Ma

In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation.

Autonomous Driving Contrastive Learning +2

WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language

1 code implementation12 Apr 2023 Zhenxiang Lin, Xidong Peng, Peishan Cong, Ge Zheng, Yujin Sun, Yuenan Hou, Xinge Zhu, Sibei Yang, Yuexin Ma

We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds.

3D visual grounding Autonomous Driving +1

One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving

no code implementations2 Apr 2023 Huitong Yang, Xuyang Bai, Xinge Zhu, Yuexin Ma

Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips, costing huge computing resources.

Autonomous Driving

CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions

no code implementations CVPR 2023 Ming Yan, Xin Wang, Yudi Dai, Siqi Shen, Chenglu Wen, Lan Xu, Yuexin Ma, Cheng Wang

The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions.

Pose Prediction

SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments

1 code implementation CVPR 2023 Yudi Dai, Yitai Lin, Xiping Lin, Chenglu Wen, Lan Xu, Hongwei Yi, Siqi Shen, Yuexin Ma, Cheng Wang

We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild.

3D Human Pose Estimation Camera Calibration +1

SCPNet: Semantic Scene Completion on Point Cloud

1 code implementation CVPR 2023 Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao

We propose a simple yet effective label rectification strategy, which uses off-the-shelf panoptic segmentation labels to remove the traces of dynamic objects in completion labels, greatly improving the performance of deep models especially for those moving objects.

3D Semantic Scene Completion Knowledge Distillation +3

Rethinking Range View Representation for LiDAR Segmentation

no code implementations ICCV 2023 Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, Ziwei Liu

We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i. e., SemanticKITTI, nuScenes, and ScribbleKITTI.

3D Semantic Segmentation Autonomous Driving +4

IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiation

1 code implementation2 Feb 2023 Juze Zhang, Ye Shi, Yuexin Ma, Lan Xu, Jingyi Yu, Jingya Wang

This paper presents an inverse kinematic optimization layer (IKOL) for 3D human pose and shape estimation that leverages the strength of both optimization- and regression-based methods within an end-to-end framework.

3D human pose and shape estimation regression

CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

1 code implementation CVPR 2023 Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, Wenping Wang

For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20. 8% and 25. 08% mIoU on nuScenes and ScanNet, respectively.

3D Semantic Segmentation Contrastive Learning +4

CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection

1 code implementation1 Dec 2022 Xidong Peng, Xinge Zhu, Yuexin Ma

Second, we present Temporal Motion Alignment module to utilize motion features in sequential frames of data to match two domains.

Pseudo Label Unsupervised Domain Adaptation

Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR

no code implementations30 Nov 2022 Peishan Cong, Yiteng Xu, Yiming Ren, Juze Zhang, Lan Xu, Jingya Wang, Jingyi Yu, Yuexin Ma

Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light.

3D Multi-Person Pose Estimation 3D Pose Estimation +2

Gait Recognition in Large-scale Free Environment via Single LiDAR

no code implementations22 Nov 2022 Xiao Han, Yiming Ren, Peishan Cong, Yujing Sun, Jingya Wang, Lan Xu, Yuexin Ma

In this paper, based on a single LiDAR, we present the Hierarchical Multi-representation Feature Interaction Network (HMRNet) for robust gait recognition.

Gait Recognition in the Wild

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection

no code implementations15 Nov 2022 Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanlong Yu, Yuexin Ma, Shengfeng He, Jia Pan

We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding.

Autonomous Driving Road Segmentation +1

Zero-shot point cloud segmentation by transferring geometric primitives

no code implementations18 Oct 2022 Runnan Chen, Xinge Zhu, Nenglun Chen, Wei Li, Yuexin Ma, Ruigang Yang, Wenping Wang

To this end, we propose a novel framework to learn the geometric primitives shared in seen and unseen categories' objects and employ a fine-grained alignment between language and the learned geometric primitives.

Point Cloud Segmentation Semantic Segmentation

GANet: Goal Area Network for Motion Forecasting

1 code implementation20 Sep 2022 Mingkun Wang, Xinge Zhu, Changqian Yu, Wei Li, Yuexin Ma, Ruochun Jin, Xiaoguang Ren, Dongchun Ren, Mingxu Wang, Wenjing Yang

In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately.

Motion Forecasting Trajectory Prediction

Vision-Centric BEV Perception: A Survey

1 code implementation4 Aug 2022 Yuexin Ma, Tai Wang, Xuyang Bai, Huitong Yang, Yuenan Hou, Yaming Wang, Yu Qiao, Ruigang Yang, Dinesh Manocha, Xinge Zhu

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion.

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

no code implementations30 May 2022 Yiming Ren, Chengfeng Zhao, 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 single LiDAR and 4 IMUs, which are set up conveniently and worn lightly.

Sensor Fusion Translation

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 Sensor Fusion

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

no code implementations22 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 regression

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 Attribute +1

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 Diversity +3

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

2 code implementations 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.

Decoder

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.

Image Classification

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

3 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.

regression

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.

regression

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 Navigate +2

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.

Collision Avoidance

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