Song Wang

I am a Ph.D. student in the College of Computer Science and Technology at Zhejiang University, advised by Prof. Jianke Zhu . Prior to that, I obtained my B.Eng. at Zhejiang University, supervised by Prof. Zhiwei Xu and Prof. Hangfang Zhao .

I am interested in computer vision and machine learning. My current research focuses on:

Various forms of academic collaboration and discussion are welcome. Feel free to reach out!

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Publications

* indicates equal contribution

In addressing the challenges of inferior performance and data closure in vision-based SSC, we introduce OccFiner, the first offboard SSC setup to solve the unreliability of the onboard model.

In this work, we have presented a scribble-based label-efficient benchmark ScribbleSC for semantic scene completion in autonomous driving. To enhance the performance in this setting, an effective scribble-supervised approach Scribble2Scene has been developed.

In this paper, we adhere to the principle of not all voxels are equal and propose hardness-aware semantic scene completion (HASSC).

In this paper, we propose MGMap, an effective approach to online HD map vectorization with the guidance of learned masks.

We propose a method named APro, designed to generate precise soft pseudo labels online for unlabeled regions within segmentation networks.

In this paper, we present an effective method, namely Point2Mask, to achieve highquality panoptic prediction using only a single random point annotation per target for training.

In this work, an efficient semantic map construction framework named LiDAR2Map, is presented with an effective BEV feature pyramid decoder and an online Camera-to-LiDAR distillation scheme.

We propose a novel approach to LiDAR semantic segmentation, which introduces a range residual image representation to capture the spatial-temporal information.

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