2023年11月29日 · CT3D是一种改进的3D目标检测框架，具有通道注意力解码模块和全局提案感知表示，可提高解码权重和检测效果。实验证明CT3D在KITTI和Waymo数据集上表现优于最先进方法。

Overview of CT3D. The raw points are first fed into the RPN for generating 3D proposals. Then the raw points along with the corresponding proposals are processed by the channel-wise Transformer composed of the proposal-to-point encoding module and the …

2021年8月23日 · In this paper, we leverage the high-quality region proposal network and a Channel-wise Transformer architecture to constitute our two-stage 3D object detection framework (CT3D) with minimal hand-crafted design.

2024年11月12日 · Improving 3D Object Detection with Channel-wise Transformer (CT3D) 介绍了一种基于通道级Transformer的3D物体检测框架，旨在利用Transformer在捕获远程空间关系方面的优势来提升检测精度，特别是对于稀疏和无序的点云数据。该方法采用了一个两阶段的检测框架，结合了Region Proposal ...

我们有两个重要贡献：1）提出端到端的两阶段3D物体检测模型：CT3D，收DETR和Transformer的影响。第一阶段是学习每个框的表示通过整合新的Transformer结构：解码基于通道数重新加权的结构。 第二个贡献是定制的Transformer。

2025年3月20日 · In this paper, our objective is to address these limitations by introducing two frameworks for 3D object detection. Firstly, we propose CT3D, which sequentially performs raw-point-based embedding, a standard Transformer encoder, and a channel-wise decoder for point features within each proposal.

CT3D is a two-stage 3D object detection framework that leverages a high-quality region proposal network and a Channel-wise Transformer architecture. The proposed CT3D simultaneously performs proposal-aware embedding and channel-wise context aggregation for the point features within each proposal.

Motivated by the recent Transformer-based 2D detection method DETR that uses CNN backbone to extract features and encoder decoder Transformer to enhance the RoI region features, we design our CT3D to generate 3D bounding boxes at the first stage, then learn per-proposal representation by incorporating a novel Transformer architecture with ...

2024年6月12日 · Firstly, we propose CT3D, which sequentially performs raw-point-based embedding, a standard Transformer encoder, and a channel-wise decoder for point features within each proposal. Secondly, we present an enhanced network called CT3D++, which incorporates geometric and semantic fusion-based embedding to extract more valuable and comprehensive ...

In this paper, we leverage the high-quality region proposal network and a Channel-wise Transformer architecture to constitute our two-stage 3D object detection framework (CT3D) with minimal hand-crafted design.