no code implementations • ICCV 2023 • Chuxin Wang, Wenfei Yang, Tianzhu Zhang
Semi-supervised 3D object detection from point cloud aims to train a detector with a small number of labeled data and a large number of unlabeled data.
no code implementations • 12 Oct 2023 • Ziyang Song, Ruijie Zhu, Chuxin Wang, Jiacheng Deng, Jianfeng He, Tianzhu Zhang
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Ranked #1 on Unsupervised Monocular Depth Estimation on KITTI-C (using extra training data)
1 code implementation • CVPR 2023 • Jiacheng Deng, Chuxin Wang, Jiahao Lu, Jianfeng He, Tianzhu Zhang, Jiyang Yu, Zhe Zhang
The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts.
Ranked #2 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)
no code implementations • ICCV 2023 • Jiahao Lu, Jiacheng Deng, Chuxin Wang, Jianfeng He, Tianzhu Zhang
Additionally, we design an affiliated transformer decoder that suppresses the interference of noise background queries and helps the foreground queries focus on instance discriminative parts to predict final segmentation results.
Ranked #2 on 3D Instance Segmentation on ScanNet(v2)
1 code implementation • CVPR 2021 • Chulin Xie, Chuxin Wang, Bo Zhang, Hao Yang, Dong Chen, Fang Wen
In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion.
Ranked #1 on Point Cloud Completion on ShapeNet (Earth Mover's Distance metric)