An adaptive weighted least squares method for indoor scene reconstruction

With the rapid development of 3D modeling and immersive simulation technologies, the accuracy of indoor environment reconstruction is receiving increasing attention. This paper proposes an indoor scene reconstruction method that preserves surface discontinuities. The method utilizes the prior of surface discontinuities to construct a step function taking values of 0 and 1 and employs the smooth approximation of this function as weights to establish an adaptive weighted least squares fitting model for normals. Given the non-convexity of the model, we present an iterative scheme that uses the depth generated from the previous iteration (with the initial depth set to zero) to fix the weights, converting it into a convex quadratic functional for solution. Experimental results demonstrate that our method can recover indoor scenes more accurately than existing technologies while preserving surface discontinuities, particularly performing well in handling large planar sections of indoor environments.