NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

• URL: http://arxiv.org/abs/2211.16799v2
• Date: Wed, 13 Sep 2023 02:48:16 GMT
• Title: NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction
• Authors: Bin Tan, Nan Xue, Tianfu Wu, Gui-Song Xia
• Abstract summary: This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration. We present a novel Neural One-PlanE RANSAC framework that exerts excellent capability to learn one-plane pose hypotheses.
• Score: 41.00845324937751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs for camera pose estimation. We present a novel Neural One-PlanE RANSAC framework (termed NOPE-SAC in short) that exerts excellent capability to learn one-plane pose hypotheses from 3D plane correspondences. Building on the top of a siamese plane detection network, our NOPE-SAC first generates putative plane correspondences with a coarse initial pose. It then feeds the learned 3D plane parameters of correspondences into shared MLPs to estimate the one-plane camera pose hypotheses, which are subsequently reweighed in a RANSAC manner to obtain the final camera pose. Because the neural one-plane pose minimizes the number of plane correspondences for adaptive pose hypotheses generation, it enables stable pose voting and reliable pose refinement in a few plane correspondences for the sparse-view inputs. In the experiments, we demonstrate that our NOPE-SAC significantly improves the camera pose estimation for the two-view inputs with severe viewpoint changes, setting several new state-of-the-art performances on two challenging benchmarks, i.e., MatterPort3D and ScanNet, for sparse-view 3D reconstruction. The source code is released at https://github.com/IceTTTb/NopeSAC for reproducible research.

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