In this paper we study the task of a single-view image-guided point cloud
completion. Existing methods have got promising results by fusing the
information of image into point cloud explicitly or implicitly. However, given
that the image has global shape information and the partial point cloud has
rich local details, We believe that both modalities need to be given equal
attention when performing modality fusion. To this end, we propose a novel
dual-channel modality fusion network for image-guided point cloud
completion(named DMF-Net), in a coarse-to-fine manner. In the first stage,
DMF-Net takes a partial point cloud and corresponding image as input to recover
a coarse point cloud. In the second stage, the coarse point cloud will be
upsampled twice with shape-aware upsampling transformer to get the dense and
complete point cloud. Extensive quantitative and qualitative experimental
results show that DMF-Net outperforms the state-of-the-art unimodal and
multimodal point cloud completion works on ShapeNet-ViPC dataset.

我们提出了一种新颖的双通道模态融合网络（DMF-Net），用于图像引导的点云完成任务，在粗到精的方式下，通过将图像和部分点云一起输入网络进行模态融合，从而实现点云的恢复和补全，实验结果表明 DMF-Net 在 ShapeNet-ViPC 数据集上的性能优于现有的单模态和多模态点云完成方法。

DMF-Net：基于双通道模态融合和形状感知上采样变换的图像导向点云补全

DMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality  Fusion and Shape-Aware Upsampling Transformer

RGB-T tracking involves the use of images from both visible and thermal
modalities. The primary objective is to adaptively leverage the relatively
dominant modality in varying conditions to achieve more robust tracking
compared to single-modality tracking. An RGB-T tracker based on mixed attention
mechanism to achieve complementary fusion of modalities (referred to as MACFT)
is proposed in this paper. In the feature extraction stage, we utilize
different transformer backbone branches to extract specific and shared
information from different modalities. By performing mixed attention operations
in the backbone to enable information interaction and self-enhancement between
the template and search images, it constructs a robust feature representation
that better understands the high-level semantic features of the target. Then,
in the feature fusion stage, a modality-adaptive fusion is achieved through a
mixed attention-based modality fusion network, which suppresses the low-quality
modality noise while enhancing the information of the dominant modality.
Evaluation on multiple RGB-T public datasets demonstrates that our proposed
tracker outperforms other RGB-T trackers on general evaluation metrics while
also being able to adapt to longterm tracking scenarios.

本文提出了一种基于混合注意机制的 RGB-T 追踪器 (MACFT)，该追踪器在特征提取和特征融合阶段分别利用不同的转换器骨干支路和混合注意操作实现多模式适应性融合，有效提高了 RGB-T 追踪的鲁棒性和适用性。