Search Results for author: Dingfu Zhou

Found 17 papers, 6 papers with code

FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection

no code implementations23 Jun 2021 Shaoqing Xu, Dingfu Zhou, Jin Fang, Junbo Yin, Zhou Bin, Liangjun Zhang

Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module.

3D Object Detection Autonomous Driving +1

MapFusion: A General Framework for 3D Object Detection with HDMaps

no code implementations10 Mar 2021 Jin Fang, Dingfu Zhou, Xibin Song, Liangjun Zhang

In this paper, we propose a simple but effective framework - MapFusion to integrate the map information into modern 3D object detector pipelines.

3D Object Detection Autonomous Driving

IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image

no code implementations5 Mar 2021 Dingfu Zhou, Xibin Song, Yuchao Dai, Junbo Yin, Feixiang Lu, Jin Fang, Miao Liao, Liangjun Zhang

3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed.

3D Object Detection Autonomous Driving +1

DVI: Depth Guided Video Inpainting for Autonomous Driving

2 code implementations ECCV 2020 Miao Liao, Feixiang Lu, Dingfu Zhou, Sibo Zhang, Wei Li, Ruigang Yang

To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud.

Autonomous Driving Image Inpainting +2

PerMO: Perceiving More at Once from a Single Image for Autonomous Driving

no code implementations16 Jul 2020 Feixiang Lu, Zongdai Liu, Xibin Song, Dingfu Zhou, Wei Li, Hui Miao, Miao Liao, Liangjun Zhang, Bin Zhou, Ruigang Yang, Dinesh Manocha

We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving.

3D Reconstruction Autonomous Driving +3

Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution

no code implementations CVPR 2020 Xibin Song, Yuchao Dai, Dingfu Zhou, Liu Liu, Wei Li, Hongdng Li, Ruigang Yang

Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss.

Depth Map Super-Resolution

Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem

1 code implementation15 Mar 2020 Liu Liu, Dylan Campbell, Hongdong Li, Dingfu Zhou, Xibin Song, Ruigang Yang

Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given.

IoU Loss for 2D/3D Object Detection

1 code implementation11 Aug 2019 Dingfu Zhou, Jin Fang, Xibin Song, Chenye Guan, Junbo Yin, Yuchao Dai, Ruigang Yang

In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage.

2D Object Detection 3D Object Detection

Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry

no code implementations3 Mar 2019 Dingfu Zhou, Yuchao Dai, Hongdong Li

Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems.

Monocular Visual Odometry

ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving

no code implementations CVPR 2019 Xibin Song, Peng Wang, Dingfu Zhou, Rui Zhu, Chenye Guan, Yuchao Dai, Hao Su, Hongdong Li, Ruigang Yang

Specifically, we first segment each car with a pre-trained Mask R-CNN, and then regress towards its 3D pose and shape based on a deformable 3D car model with or without using semantic keypoints.

3D Car Instance Understanding Autonomous Driving

Augmented LiDAR Simulator for Autonomous Driving

no code implementations17 Nov 2018 Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu Ma, Liang Wang, Ruigang Yang

Instead, we can simply deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background point cloud, based on which annotated point cloud can be automatically generated.

Autonomous Driving

RealPoint3D: Point Cloud Generation from a Single Image with Complex Background

1 code implementation8 Sep 2018 Yan Xia, Yang Zhang, Dingfu Zhou, Xinyu Huang, Cheng Wang, Ruigang Yang

Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud.

Point Cloud Generation

The ApolloScape Open Dataset for Autonomous Driving and its Application

2 code implementations16 Mar 2018 Xinyu Huang, Peng Wang, Xinjing Cheng, Dingfu Zhou, Qichuan Geng, Ruigang Yang

In this paper, we provide a sensor fusion scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robust self-localization and semantic segmentation for autonomous driving.

Autonomous Driving Instance Segmentation +3

Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion

no code implementations12 Jul 2017 Dingfu Zhou, Yuchao Dai, Hongdong Li

First, we prove that there indeed exist enough degrees of freedom to apply pixel-wise local homography for stereo rectification.

3D Reconstruction Rectification +2

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