This paper introduces CN-RMA, a novel approach for 3D indoor object detection from multi-view images. We observe the key challenge as the ambiguity of image and 3D correspondence without explicit geometry to provide occlusion information. To address this issue, CN-RMA leverages the synergy of 3D reconstruction networks and 3D object detection networks, where the reconstruction network provides a rough Truncated Signed Distance Function (TSDF) and guides image features to vote to 3D space correctly in an end-to-end manner. Specifically, we associate weights to sampled points of each ray through ray marching, representing the contribution of a pixel in an image to corresponding 3D locations. Such weights are determined by the predicted signed distances so that image features vote only to regions near the reconstructed surface. Our method achieves state-of-the-art performance in 3D object detection from multi-view images, as measured by mAP@0.25 and mAP@0.5 on the ScanNet and ARKitScenes datasets. The code and models are released at https://github.com/SerCharles/CN-RMA.

本文介绍了CN-RMA，一种用于从多视角图像进行3D室内物体检测的新方法。该方法通过结合3D重建网络和3D物体检测网络的协同作用来解决图像和3D对应的歧义性问题，其中重建网络提供粗糙的裁剪符号距离函数（TSDF），并以端到端的方式引导图像特征正确投票到3D空间。

CN-RMA：基于光线行进聚合的三维室内多视图物体检测融合网络