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Chuanwei Zhou

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4 papers

2 author rows

IROS Conference 2024 Conference Paper

SGNet: Salient Geometric Network for Point Cloud Registration

Qianliang Wu
Yaqing Ding 0001
Lei Luo 0001
Haobo Jiang
Shuo Gu
Chuanwei Zhou
Jin Xie 0001
Jian Yang 0003

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

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AAAI Conference 2023 Conference Paper

Exploratory Inference Learning for Scribble Supervised Semantic Segmentation

Chuanwei Zhou
Zhen Cui
Chunyan Xu
Cao Han
Jian Yang

Scribble supervised semantic segmentation has achieved great advances in pseudo label exploitation, yet suffers insufficient label exploration for the mass of unannotated regions. In this work, we propose a novel exploratory inference learning (EIL) framework, which facilitates efficient probing on unlabeled pixels and promotes selecting confident candidates for boosting the evolved segmentation. The exploration of unannotated regions is formulated as an iterative decision-making process, where a policy searcher learns to infer in the unknown space and the reward to the exploratory policy is based on a contrastive measurement of candidates. In particular, we devise the contrastive reward with the intra-class attraction and the inter-class repulsion in the feature space w.r.t the pseudo labels. The unlabeled exploration and the labeled exploitation are jointly balanced to improve the segmentation, and framed in a close-looping end-to-end network. Comprehensive evaluations on the benchmark datasets (PASCAL VOC 2012 and PASCAL Context) demonstrate the superiority of our proposed EIL when compared with other state-of-the-art methods for the scribble-supervised semantic segmentation problem.

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AAAI Conference 2023 Conference Paper

Progressive Bayesian Inference for Scribble-Supervised Semantic Segmentation

Chuanwei Zhou
Chunyan Xu
Zhen Cui

The scribble-supervised semantic segmentation is an important yet challenging task in the field of computer vision. To deal with the pixel-wise sparse annotation problem, we propose a Progressive Bayesian Inference (PBI) framework to boost the performance of the scribble-supervised semantic segmentation, which can effectively infer the semantic distribution of these unlabeled pixels to guide the optimization of the segmentation network. The PBI dynamically improves the model learning from two aspects: the Bayesian inference module (i.e., semantic distribution learning) and the pixel-wise segmenter (i.e., model updating). Specifically, we effectively infer the semantic probability distribution of these unlabeled pixels with our designed Bayesian inference module, where its guidance is estimated through the Bayesian expectation maximization under the situation of partially observed data. The segmenter can be progressively improved under the joint guidance of the original scribble information and the learned semantic distribution. The segmenter optimization and semantic distribution promotion are encapsulated into a unified architecture where they could improve each other with mutual evolution in a progressive fashion. Comprehensive evaluations of several benchmark datasets demonstrate the effectiveness and superiority of our proposed PBI when compared with other state-of-the-art methods applied to the scribble-supervised semantic segmentation task.

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AAAI Conference 2021 Conference Paper

Deep Wasserstein Graph Discriminant Learning for Graph Classification

Tong Zhang
Yun Wang
Zhen Cui
Chuanwei Zhou
Baoliang Cui
Haikuan Huang
Jian Yang

Graph topological structures are crucial to distinguish different-class graphs. In this work, we propose a deep Wasserstein graph discriminant learning (WGDL) framework to learn discriminative embeddings of graphs in Wassersteinmetric (W-metric) matching space. In order to bypass the calculation of W-metric class centers in discriminant analysis, as well as better support batch process learning, we introduce a reference set of graphs (aka graph dictionary) to express those representative graph samples (aka dictionary keys). On the bridge of graph dictionary, every input graph can be projected into the latent dictionary space through our proposed Wasserstein graph transformation (WGT). In WGT, we formulate inter-graph distance in W-metric space by virtue of the optimal transport (OT) principle, which effectively expresses the correlations of cross-graph structures. To make WGDL better representation ability, we dynamically update graph dictionary during training by maximizing the Wasserstein Discriminant loss, i. e. the ratio of inter-class versus intra-class Wasserstein distance. To evaluate our WGDL method, comprehensive experiments are conducted on six graph classification datasets. Experimental results demonstrate the effectiveness of our WGDL, and state-of-the-art performance.

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