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・IP 属地广东 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 implement this idea by designing the IPE transformer, which enjoys stronger generalization powers across arbitrary input sizes. To verify its effectiveness, we integrate the newly-designed transformer into NLSPN and GuideNet, two remarkable depth completion networks. The experimental result on a ...
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 Depth Completion benchmark和室内NYUv2数据集上表现优于当前CNN方法,并且比纯Transform...
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...
另一个研究方向是depth completion,其目的是稀疏的已知注释中填充未知的深度值。Imran等人[15]提出了一种分层方法,将前景和背景区域与激光雷达数据分开进行外推。在我们的深度细化方法中,mask和1-mask区域都在校正不准确的深度值的同时进行inpainted/ outpainted,然后进行合并以获得准确的边界。
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and...
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 dept...
TDT-DSR[Paper]: Depth Super-Resolution by Texture-Depth Transformer (In 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021), Yao, Chao, Shuaiyong Zhang, Mengyao Yang, Meiqin Liu, and Junpeng Qi CTKT[Paper]: Learning Scene Structure Guidance via Cross-Task Knowledge Transfer...
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,仅使用原始深度图像作为输入,本文提出了一种基于 CNN 的自补全模块 (SCM),具有跨尺度注意力 (CSA) 块来恢复深度图像的粗略版本。
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...