具体来说,提出了一种用于RGB引导的室内深度补全的混合CNN-Transformer网络,以从粗到细的方式实现高准确的深度图。 二、Method Framework 图1 显示了所提出的用于 RGB 引导深度图像补全的混合 CNN-Transformer 网络的总体框架。所提出的网络包括两个阶段,以从粗到细的方式实现深度补全。 在第一阶段,仅使用原始深度...
《CompletionFormer: Depth Completion with Convolutions and Vision Transformers》一句话总结论文:该论文提出了一种深度完成模型,称为CompletionFormer,利用Joint Convolutional Attention和Transformer block(JCAT)在Convolutional层与Transformer层之间建立有效连接,从而使得模型同时兼具局部连接和全局内容,该模型在室外KITTI ...
在实际的深度补全过程中,首先需要准备数据集,包括稀疏的深度图像和对应的完整彩色图像。然后,可以选择合适的深度补全网络架构,如U-Net、Fully Convolutional Networks (FCNs) 或者最近的基于Transformer的模型。模型训练通常涉及损失函数的设计,如像素级的L1或L2损失,以及可能的结构相似性损失。训练完成后,模型可以在新的...
Transformermulti-source feature fusiondepth completionFusing multiple sensor perceptions, specifically LiDAR and camera, is a prevalent method for target recognition in autonomous driving systems. Traditional object detection algorithms are limited by the sparse nature of LiDAR point clouds, resulting in ...
Since the RGB images con- tain rich semantic cues that are critical for filling unknown depth, some works [22, 23, 30, 42] utilize the RGB informa- tion to guide depth completion. Although many advanced networks such as ResNet [10] and Transformer [8, ...
transformer block, SparseFormer, that fuses 3D landmarks with deep visual features to produce dense depth. The SparseFormer has a global receptive field, making the module especially effective for depth completion with low-density and non-uniform landmarks. To address the issue of depth outliers...
However, this method also regards guided depth completion as a guided restoration task, which can't exploit 3D geometry information. In this work, we devise the transformer-based PointDC to effec- tively extract and propagate the 3D geometry information contained in the input sparse depth...
作者的主要工作是提出了LayerDrop的方法,即一种结构化的dropout的方法来对transformer模型进行训练,从而在不需要fine-tune的情况下选择一个大网络的子网络。 这篇paper方法的核心是通过Dropout来...Indoor Depth Completion with Boundary Consistency and Self-Attention Indoor Depth Completion with Boundary Consistency...
多模态笔记:CompletionFormer: Depth Completion with Convolutions and Vision Transformers Rienzi 不得不时时刻刻与自己对峙的人 RGB+sparse depth编辑于 2023-11-13 23:01・广东 Transformer 多模态学习 计算机视觉 赞同3 条评论 分享喜欢收藏申请转载 ...
We introduce a transformer block, SparseFormer, that fuses 3D landmarks with deep visual features to produce dense depth. The SparseFormer has a global receptive field, making the module especially effective for depth completion with low-density and non-uniform landmarks. To address the issue ...