23 citations · 47 across the 11 of their papers we have counts for
11 papers
Enhancing 3D LiDAR Segmentation by Shaping Dense and Accurate 2D Semantic Predictions
Xiaoyu Dong, Tiankui Xian, Wanshui Gan +1
Semantic segmentation of 3D LiDAR point clouds is important in urban remote sensing for understanding real-world street environments. This task, by projecting LiDAR point clouds an…
Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis
Ziteng Cui, Shuhong Liu, Xiaoyu Dong +4
High-quality image acquisition in real-world environments remains challenging due to complex illumination variations and inherent limitations of camera imaging pipelines. These iss…
Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations
Xiaoyu Dong, Jiahuan Li, Ziteng Cui +1
Cross-modal super-resolution (SR) on real-world misaligned data is challenging, as only unlabeled low-resolution (LR) source and high-resolution (HR) guide images with complex spat…
Deep Lookup Network
Yulan Guo, Longguang Wang, Wendong Mao +4
Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operatio…
Understanding Dark Scenes by Contrasting Multi-Modal Observations
Xiaoyu Dong, Naoto Yokoya
Understanding dark scenes based on multi-modal image data is challenging, as both the visible and auxiliary modalities provide limited semantic information for the task. Previous m…
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
Runsheng Xu, Xin Xia, Jinlong Li +10
Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks th…
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
Xiaoyu Dong, Naoto Yokoya, Longguang Wang +1
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high…
Pik-Fix: Restoring and Colorizing Old Photos
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +6
Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer…
ROMNet: Renovate the Old Memories
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +5
Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cr…
Unsupervised Degradation Representation Learning for Blind Super-Resolution
Longguang Wang, Yingqian Wang, Xiaoyu Dong +4
Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these meth…
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Longguang Wang, Xiaoyu Dong, Yingqian Wang +4
Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-…