no code implementations • 27 Mar 2024 • Ruikai Cui, Weizhe Liu, Weixuan Sun, Senbo Wang, Taizhang Shang, Yang Li, Xibin Song, Han Yan, Zhennan Wu, Shenzhou Chen, Hongdong Li, Pan Ji
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints.
1 code implementation • 19 Sep 2023 • Jiaxin Wei, Xibin Song, Weizhe Liu, Laurent Kneip, Hongdong Li, Pan Ji
While showing promising results, recent RGB-D camera-based category-level object pose estimation methods have restricted applications due to the heavy reliance on depth sensors.
1 code implementation • 31 Jul 2023 • Zhelun Shen, Xibin Song, Yuchao Dai, Dingfu Zhou, Zhibo Rao, Liangjun Zhang
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others.
no code implementations • 24 Mar 2022 • Zhiyuan Zhang, Jiadai Sun, Yuchao Dai, Dingfu Zhou, Xibin Song, Mingyi He
Existing correspondences-free methods generally learn the holistic representation of the entire point cloud, which is fragile for partial and noisy point clouds.
no code implementations • 28 Oct 2021 • Zhiyuan Zhang, Jiadai Sun, Yuchao Dai, Dingfu Zhou, Xibin Song, Mingyi He
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 challenge because existing hard assignment methods cannot deal with outliers naturally.
2 code implementations • ICCV 2021 • Lina Liu, Xibin Song, Mengmeng Wang, Yong liu, Liangjun Zhang
Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected.
no code implementations • 10 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.
no code implementations • 5 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.
Ranked #19 on Monocular 3D Object Detection on KITTI Cars Moderate
no code implementations • 15 Dec 2020 • Lina Liu, Xibin Song, Xiaoyang Lyu, Junwei Diao, Mengmeng Wang, Yong liu, Liangjun Zhang
Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input.
no code implementations • 16 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.
2 code implementations • 23 Jun 2020 • Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao, Dingfu Zhou, Liangjun Zhang
First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
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.
1 code implementation • 15 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.
1 code implementation • 11 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.
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.
no code implementations • 27 Aug 2018 • Xibin Song, Yuchao Dai, Xueying Qin
However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used; and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as $\times 8, \times 16$.
no code implementations • 7 Jul 2016 • Xibin Song, Yuchao Dai, Xueying Qin
In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution.