CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view Images
CVPR 2024(2024)
摘要
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.
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