1 code implementation • 17 Oct 2023 • Ruibo Li, Chi Zhang, Zhe Wang, Chunhua Shen, Guosheng Lin
By rigidly aligning each region with its potential counterpart in the target point cloud, we obtain a region-specific rigid transformation to generate its pseudo flow labels.
no code implementations • CVPR 2023 • Ruibo Li, Hanyu Shi, Ziang Fu, Zhe Wang, Guosheng Lin
To this end, we propose a two-stage weakly supervised approach, where the segmentation model trained with the incomplete binary masks in Stage1 will facilitate the self-supervised learning of the motion prediction network in Stage2 by estimating possible moving foregrounds in advance.
1 code implementation • ICCV 2023 • Ze Yang, Ruibo Li, Evan Ling, Chi Zhang, Yiming Wang, Dezhao Huang, Keng Teck Ma, Minhoe Hur, Guosheng Lin
To address this issue, we propose a new label-guided knowledge distillation (LGKD) loss, where the old model output is expanded and transplanted (with the guidance of the ground truth label) to form a semantically appropriate class correspondence with the new model output.
Ranked #1 on Continual Semantic Segmentation on ScanNet
1 code implementation • 18 Nov 2022 • Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin
With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision.
1 code implementation • ICCV 2023 • Shichao Dong, Ruibo Li, Jiacheng Wei, Fayao Liu, Guosheng Lin
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas.
Ranked #22 on 3D Instance Segmentation on ScanNet(v2)
1 code implementation • 23 Mar 2022 • Ze Yang, Chi Zhang, Ruibo Li, Yi Xu, Guosheng Lin
Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed.
no code implementations • CVPR 2022 • Ruibo Li, Chi Zhang, Guosheng Lin, Zhe Wang, Chunhua Shen
In this work, we focus on scene flow learning on point clouds in a self-supervised manner.
1 code implementation • CVPR 2022 • Hanyu Shi, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin
We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data.
1 code implementation • NeurIPS 2021 • Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin
It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e. g., body shape) of the target mesh.
no code implementations • ICCV 2021 • Chi Zhang, Henghui Ding, Guosheng Lin, Ruibo Li, Changhu Wang, Chunhua Shen
Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.
no code implementations • CVPR 2021 • Ruibo Li, Guosheng Lin, Lihua Xie
Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention.
Self-Supervised Learning Self-supervised Scene Flow Estimation
no code implementations • CVPR 2021 • Ruibo Li, Guosheng Lin, Tong He, Fayao Liu, Chunhua Shen
Scene flow in 3D point clouds plays an important role in understanding dynamic environments.
1 code implementation • 11 Jul 2018 • Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Lingxiao Hang
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
no code implementations • CVPR 2018 • Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Yang Xiao, Ruibo Li, Zhenbo Luo
In this paper we study the problem of monocular relative depth perception in the wild.