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Dayang Hao

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3 papers

2 author rows

AAAI Conference 2023 Conference Paper

PUPS: Point Cloud Unified Panoptic Segmentation

Shihao Su
Jianyun Xu
Huanyu Wang
Zhenwei Miao
Xin Zhan
Dayang Hao
Xi Li

Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points. Previous approaches treat semantic and instance segmentation as surrogate tasks, and they either use clustering methods or bounding boxes to gather instance groupings with costly computation and hand-craft designs in the instance segmentation task. In this paper, we propose a simple but effective point cloud unified panoptic segmentation (PUPS) framework, which use a set of point-level classifiers to directly predict semantic and instance groupings in an end-to-end manner. To realize PUPS, we introduce bipartite matching to our training pipeline so that our classifiers are able to exclusively predict groupings of instances, getting rid of hand-crafted designs, e.g. anchors and Non-Maximum Suppression (NMS). In order to achieve better grouping results, we utilize a transformer decoder to iteratively refine the point classifiers and develop a context-aware CutMix augmentation to overcome the class imbalance problem. As a result, PUPS achieves 1st place on the leader board of SemanticKITTI panoptic segmentation task and state-of-the-art results on nuScenes.

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ICRA Conference 2021 Conference Paper

MDANet: Multi-Modal Deep Aggregation Network for Depth Completion

Yanjie Ke
Kun Li
Wei Yang 0011
Zhenbo Xu
Dayang Hao
Liusheng Huang
Gang Wang

Depth completion aims to recover the dense depth map from sparse depth data and RGB image respectively. However, due to the huge difference between the multi-modal signal input, vanilla convolutional neural network and simple fusion strategy cannot extract features from sparse data and aggregate multi-modal information effectively. To tackle this problem, we design a novel network architecture that takes full advantage of multi-modal features for depth completion. An effective Pre-completion algorithm is first put forward to increase the density of the input depth map and to provide distribution priors. Moreover, to effectively fuse the image features and the depth features, we propose a multi-modal deep aggregation block that consists of multiple connection and aggregation pathways for deeper fusion. Furthermore, based on the intuition that semantic image features are beneficial for accurate contour, we introduce the deformable guided fusion layer to guide the generation of the dense depth map. The resulting architecture, called MDANet, outperforms all the stateof-the-art methods on the popular KITTI Depth Completion Benchmark, meanwhile with fewer parameters than recent methods. The code of this work will be available at https://github.com/USTC-Keyanjie/MDANet_ICRA2021.

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ICRA Conference 2021 Conference Paper

Robust SRIF-based LiDAR-IMU Localization for Autonomous Vehicles

Kun Li
Zhanpeng Ouyang
Lan Hu
Dayang Hao
Laurent Kneip

We present a tightly-coupled multi-sensor fusion architecture for autonomous vehicle applications, which achieves centimetre-level accuracy and high robustness in various scenarios. In order to realize robust and accurate point-cloud feature matching we propose a novel method for extracting structural, highly discriminative features from LiDAR point clouds. For high frequency motion prediction and noise propagation, we use incremental on-manifold IMU pre-integration. We also adopt a multi-frame sliding window square root inverse filter, so that the system maintains numerically stable results under the premise of limited power consumption. To verify our methodology, we test the fusion algorithm in multiple applications and platforms equipped with a LiDAR-IMU system. Our results demonstrate that our fusion framework attains state-of-the-art localization accuracy, high robustness and a good generalization ability.

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