MirageRoom: 3D Scene Segmentation with 2D Pre-Trained Models by Mirage Projection

Nowadays, leveraging 2D images and pre-trained mod- els to guide 3D point cloud feature representation has shown a remarkable potential to boost the performance of 3D fundamental models. While some works rely on additional data such as 2D real-world images and their corre- sponding camera poses, recent studies target at using point cloud exclusively by designing 3D-to-2D projection. How- ever, in the indoor scene scenario, existing 3D-to-2D pro- jection strategies suffer from severe occlusions and incoher- ence, which fail to contain sufficient information for fine- grained point cloud segmentation task. In this paper, we ar- gue that the crux of the matter resides in the basic premise of existing projection strategies that the medium is homo- geneous, thereby projection rays propagate along straight lines and behind objects are occluded by front ones. In- spired by the phenomenon of mirage where the occluded objects are exposed by distorted light rays due to heteroge- neous medium refraction rate, we propose MirageRoom by designing parametric mirage projection with heterogeneous medium to obtain series of projected images with various distorted degrees. We further develop a masked reprojection module across 2D and 3D latent space to bridge the gap between pre-trained 2D backbone and 3D point-wise features. Both quantitative and qualitative experimental re- sults on S3DIS and ScanNet V2 demonstrate the effective- ness of our method.
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Code will be available here.