PIPS: Planar Instance 3D Reconstruction Leveraging Planar Structural Priors

Abstract

Planar structures, ubiquitous in man-made indoor environments, enable compact and accurate scene abstraction for various downstream tasks. Recent methods distill planar features into learning-based MVS geometries to obtain coherent 3D plane estimation from multi-view inputs. However, the lack of explicit planar instance definitions hinders semantic–geometry alignment, leading to distorted geometry and mismatched semantics. To address this, we propose PIPS, a planar-instance 3D reconstruction method that leverages planar structural priors for both single-view planar segmentation (SGPS module) and multi-view instance association (MVPI module). The planar instance point clouds are regularized by planar distances and then converted into complete planar meshes via an instance-level planar meshing strategy. Extensive experiments on hundreds of indoor scenes demonstrate the superior performance of our method, which is less dependent on annotations and requires no feature optimization. The effectiveness of each component is further verified through comprehensive ablation studies. The project page of PIPS is available at https://pips325.github.io.