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Haozhe Cheng

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AAAI Conference 2026 Conference Paper

Point-SRA: Self-Representation Alignment for 3D Representation Learning

Lintong Wei
Jian Lu
Haozhe Cheng
Jihua Zhu
Kaibing Zhang

Masked autoencoders (MAE) have become a dominant paradigm in 3D representation learning, setting new performance benchmarks across various downstream tasks. Existing methods with fixed mask ratios neglect multi-level representational correlations and intrinsic geometric structures, while relying on point-wise reconstruction assumptions that conflict with the diversity of point cloud. To address these issues, we propose a 3D representation learning method, termed Point-SRA, which aligns representations through self-distillation and probabilistic modeling. Specifically, we assign different masking ratios to the MAE to capture complementary geometric and semantic information, while the MeanFlow Transformer (MFT) leverages cross-modal conditional embeddings to enable diverse probabilistic reconstruction. Our analysis further reveals that representations at different time steps in MFT also exhibit complementarity. Therefore, a Dual Self-Representation Alignment mechanism is proposed at both the MAE and MFT levels. Finally, we design a Flow-Conditioned Fine-Tuning Architecture to fully exploit the point cloud distribution learned via MeanFlow. Point-SRA outperforms Point-MAE by 5.37% on ScanObjectNN. On intracranial aneurysm segmentation, it reaches 96.07% mean IoU for arteries and 86.87% for aneurysms. For 3D object detection, Point-SRA achieves 47.3% AP@50, surpassing MaskPoint by 5.12%.

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IROS Conference 2024 Conference Paper

Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification

Naiwen Hu
Haozhe Cheng
Yifan Xie
Pengcheng Shi
Jihua Zhu

3D contrastive representation learning has exhibited remarkable efficacy across various downstream tasks. However, existing contrastive learning paradigms based on cosine similarity fail to deeply explore the potential intra-modal hierarchical and cross-modal semantic correlations about multi-modal data in Euclidean space. In response, we seek solutions in hyperbolic space and propose a hyperbolic image-and-pointcloud contrastive learning method (HyperIPC). For the intra-modal branch, we rely on the intrinsic geometric structure to explore the hyperbolic embedding representation of point cloud to capture invariant features. For the cross-modal branch, we leverage images to guide the point cloud in establishing strong semantic hierarchical correlations. Empirical experiments underscore the outstanding classification performance of HyperIPC. Notably, HyperIPC enhances object classification results by 2. 8% and few-shot classification outcomes by 5. 9% on ScanObjectNN compared to the baseline. Furthermore, ablation studies and confirmatory testing validate the rationality of HyperIPC’s parameter settings and the effectiveness of its submodules.

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EAAI Journal 2021 Journal Article

PTANet: Triple Attention Network for point cloud semantic segmentation

Haozhe Cheng
Jian Lu
Maoxin Luo
Wei Liu
Kaibing Zhang

For 3D point cloud semantic segmentation, mining more informative features to enrich contextual representation is regarded as the key to achieve better segmentation performance. Unfortunately, the existing point cloud segmentation network lacks a comprehensive consideration of utilizing contextual information from both global and local perspectives, thus failing to fully explore the contextual representation, which prevents fine-grained objects from being accurately recognized. Therefore, this paper proposes a neural network dubbed PTANet that effectively enriches the contextual representation to improve segmentation accuracy. PTANet possesses two uncomplicated and effective parts: Triple Attention Block and Density Scale Learning Strategy. Triple Attention Block consists of three sub modules: 1. Position attention module updates feature maps by modeling the interdependency between the spatial positions of each point. 2. Channel attention module recalibrates the original feature in the light of the correlation weight between the channels of feature maps to enrich the contextual representation globally. 3. Local Region attention module calculates the interdependence weight between local neighbors to further complement the local feature information. In addition, to alleviate the adverse effect of non-uniform distribution of point cloud on the inference results, Density Scale Learning Strategy applies the kernel density estimation under the adaptive bandwidth to fit the density scale of each point. In particular, the density scale weighted to the feature maps can also supplement the density information for local features. The experimental performance verifies the effectiveness of PTANet. It obtained 86. 1% mIoU on ShapeNet, 62. 4% mIoU on ScannetV2, and 87. 9% OA on S3DIS.

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