PMVC: Promoting Multi-View Consistency for 3D Scene Reconstruction

Reconstructing the geometry of a 3D scene from its multi-view 2D observations has been a central task of 3D computer vision. Recent methods based on neural rendering that use implicit shape representations such as the neural Signed Distance Function (SDF), have shown impressive performance. However, they fall short in recovering fine details in the scene, especially when employing a multilayer perceptron (MLP) as the interpolation function for the SDF representation. Per-frame image normal or depth-map prediction have been utilized to tackle this issue, but these learning-based depth/normal predictions are based on a single image frame only, hence overlooking the underlying multiview consistency of the scene, leading to inconsistent erroneous 3D reconstruction. To mitigate this problem, we propose to leverage multi-view deep features computed on the images. In addition, we employ an adaptive sampling strategy that assesses the fidelity of the multi-view image consistency. Our approach outperforms current state-of-the-art methods, delivering an accurate and robust scene representation with particularly enhanced details. The effectiveness of our proposed approach is evaluated by extensive experiments conducted on the ScanNet and Replica datasets, showing superior performance than the current state-of-the-art.