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arXiv Computer Vision By Jie Tang, Pingping Xie, Jian Li, Ping Tan

Gaussian Belief Propagation Network for Depth Completion

Read the original on arXiv Computer Vision →

The paper introduces the Gaussian Belief Propagation Network (GBPN) for depth completion, a hybrid framework that combines deep learning with probabilistic graphical models. GBPN constructs a scene‑specific Markov Random Field via a Graphical Model Construction Network, then infers dense depth distributions using Gaussian Belief Propagation with a serial & parallel message passing scheme. Experiments show GBPN achieves state‑of‑the‑art performance on NYUv2 and KITTI, demonstrating robustness and generalizability across different sparsity levels and patterns.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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