Zobrazeno 1 - 10
of 598
pro vyhledávání: '"Ma, Lizhuang"'
We propose DrivingForward, a feed-forward Gaussian Splatting model that reconstructs driving scenes from flexible surround-view input. Driving scene images from vehicle-mounted cameras are typically sparse, with limited overlap, and the movement of t
Externí odkaz:
http://arxiv.org/abs/2409.12753
3D Gaussian Splatting (3DGS) is a recent explicit 3D representation that has achieved high-quality reconstruction and real-time rendering of complex scenes. However, the rasterization pipeline still suffers from unnecessary overhead resulting from av
Externí odkaz:
http://arxiv.org/abs/2409.08669
In recent years, the development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series playing a crucial role. Inspired by model pruning which lightens large
Externí odkaz:
http://arxiv.org/abs/2409.06633
Autor:
Yang, Hao, Zhou, Qianyu, Sun, Haijia, Li, Xiangtai, Liu, Fengqi, Lu, Xuequan, Ma, Lizhuang, Yan, Shuicheng
Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification (PCC) models toward unseen domains. However, they often suffer from limited receptive fields or quadratic complexity due to the use of
Externí odkaz:
http://arxiv.org/abs/2408.13574
Surface defect detection is significant in industrial production. However, detecting defects with varying textures and anomaly classes during the test time is challenging. This arises due to the differences in data distributions between source and ta
Externí odkaz:
http://arxiv.org/abs/2408.09494
Autor:
Jin, Yizhang, Li, Jian, Zhang, Jiangning, Hu, Jianlong, Gan, Zhenye, Tan, Xin, Liu, Yong, Wang, Yabiao, Wang, Chengjie, Ma, Lizhuang
Visual Spatial Description (VSD) aims to generate texts that describe the spatial relationships between objects within images. Traditional visual spatial relationship classification (VSRC) methods typically output the spatial relationship between two
Externí odkaz:
http://arxiv.org/abs/2408.04957
Autor:
Jiang, Jincen, Zhou, Qianyu, Li, Yuhang, Lu, Xuequan, Wang, Meili, Ma, Lizhuang, Chang, Jian, Zhang, Jian Jun
Recent point cloud understanding research suffers from performance drops on unseen data, due to the distribution shifts across different domains. While recent studies use Domain Generalization (DG) techniques to mitigate this by learning domain-invar
Externí odkaz:
http://arxiv.org/abs/2407.08801
Autor:
Hao, Jinkun, Tang, Junshu, Zhang, Jiangning, Yi, Ran, Hong, Yijia, Li, Moran, Cao, Weijian, Wang, Yating, Ma, Lizhuang
While recent works have achieved great success on one-shot 3D common object generation, high quality and fidelity 3D head generation from a single image remains a great challenge. Previous text-based methods for generating 3D heads were limited by te
Externí odkaz:
http://arxiv.org/abs/2406.16710
Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has becom
Externí odkaz:
http://arxiv.org/abs/2406.10531
Autor:
Wang, Chengjie, Zhu, Haokun, Peng, Jinlong, Wang, Yue, Yi, Ran, Wu, Yunsheng, Ma, Lizhuang, Zhang, Jiangning
Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. Yet, both RGB and 3D data are crucial for anomaly detection, and the datasets are seldom completely clean in practical scenarios. T
Externí odkaz:
http://arxiv.org/abs/2406.02263