55 citations · 136 across the 18 of their papers we have counts for
15 papers · 1 filter
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Zhelun Shen, Xibin Song, Yuchao Dai +3
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and gene…
A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +3
3D point cloud registration in remote sensing field has been greatly advanced by deep learning based methods, where the rigid transformation is either directly regressed from the t…
End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +3
Even though considerable progress has been made in deep learning-based 3D point cloud processing, how to obtain accurate correspondences for robust registration remains a major cha…
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
Lina Liu, Xibin Song, Mengmeng Wang +2
Remarkable results have been achieved by DCNN based self-supervised depth estimation approaches. However, most of these approaches can only handle either day-time or night-time ima…
MapFusion: A General Framework for 3D Object Detection with HDMaps
Jin Fang, Dingfu Zhou, Xibin Song +1
3D object detection is a key perception component in autonomous driving. Most recent approaches are based on Lidar sensors only or fused with cameras. Maps (e.g., High Definition M…
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
Dingfu Zhou, Xibin Song, Yuchao Dai +5
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous a…
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
Lina Liu, Xibin Song, Xiaoyang Lyu +4
Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate depth completion as a one…
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving
Feixiang Lu, Zongdai Liu, Xibin Song +8
We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving. Our approach combines the streng…
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Xibin Song, Yuchao Dai, Dingfu Zhou +4
Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a maj…
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
Zhelun Shen, Yuchao Dai, Xibin Song +3
Existing deep learning based stereo matching methods either focus on achieving optimal performances on the target dataset while with poor generalization for other datasets or focus…
Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem
Liu Liu, Dylan Campbell, Hongdong Li +3
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. When the correspond…
IoU Loss for 2D/3D Object Detection
Dingfu Zhou, Jin Fang, Xibin Song +4
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 sta…
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving
Xibin Song, Peng Wang, Dingfu Zhou +6
Autonomous driving has attracted remarkable attention from both industry and academia. An important task is to estimate 3D properties(e.g.translation, rotation and shape) of a movi…
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
Xibin Song, Yuchao Dai, Xueying Qin
Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist t…
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
Xibin Song, Yuchao Dai, Xueying Qin
Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image…