Gaussian Belief Propagation Network for Depth Completion
Jie Tang, Pingping Xie, Jian Li, Ping Tan
TL;DR
The paper introduces GBPN, a hybrid deep learning and probabilistic graphical model framework for depth completion that dynamically constructs scene-specific MRFs and uses Gaussian Belief Propagation for superior dense depth prediction.
Contribution
It presents a novel hybrid framework combining deep learning with probabilistic graphical models, including a dynamic MRF construction and an enhanced GBP inference scheme, for improved depth completion.
Findings
Achieves state-of-the-art results on NYUv2 and KITTI benchmarks.
Demonstrates robustness across different sparsity levels and patterns.
Outperforms existing methods in accuracy and generalization.
Abstract
Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning methods have achieved state-of-the-art (SOTA), effectively handling the sparse and irregular nature of input depth data in deep networks remains a significant challenge, often limiting performance, especially under high sparsity. To overcome this limitation, we introduce the Gaussian Belief Propagation Network (GBPN), a novel hybrid framework synergistically integrating deep learning with probabilistic graphical models for end-to-end depth completion. Specifically, a scene-specific Markov Random Field (MRF) is dynamically constructed by the Graphical Model Construction Network (GMCN), and then inferred via Gaussian Belief Propagation (GBP) to yield the dense depth distribution. Crucially, the GMCN learns to construct not only the data-dependent potentials of MRF…
Peer Reviews
Decision·Submitted to ICLR 2026
1. Framing deep completion as probabilistic inference on dynamically constructed graph models offers a theoretically sound approach for handling sparse and irregular inputs. Extensive evaluations under varying sparsity levels, noise conditions, and cross-dataset settings show that the proposed framework achieves stronger robustness than pure end-to-end regression models. 2. The proposed method not only learns the MRF parameters but also infers the graph structure by predicting non-local edges, w
1. The ablation study (Table 2) is presented in not clear, making it difficult to verify the contribution of each model component. 2. Although the paper provides a thorough empirical comparison with competitors such as BP-Net and demonstrates clear advantages in accuracy and robustness, the discussion does not move beyond empirical evidence and lacks a compelling conceptual justification. The authors do not clearly explain why their MRF+GBP paradigm is theoretically or conceptually superior to
1. The proposed method achieves state-of-the-art performance on both indoor and outdoor datasets. 2. It shows superior robustness across varying depth sparsity levels compared to existing approaches. 3. The paper provides a comprehensive analysis and extensive experimental results in the supplementary material, which further supports the validity of the proposed approach.
1. In Figure 1, it is recommended to add essential legends for better clarity, such as explaining the meaning of “T” in the top-middle and the significance of the green, blue, and orange lines. 2. The Method section currently occupies a substantial portion of the paper, leaving limited space for the Experiment section. It is suggested to compress the Method section to allow more room for presenting additional experimental results. 3. The influence of local edges and GBP iterations should be anal
The proposed method achieves SOTA performance on public benchmarks, KITTI and NYUv2 The authors validate the effectiveness of the proposed modules and the robustness over sparsity. The authors also provide detailed information regarding the method, like model structure, proof, parameters, etc. The idea of using use Gaussian Belief Propagation for inference is interesting, with strong motivation from previous methods.
- The strategy, MRF for depth estimation, has been explored before [1][2]. The authors should provide some discussions. - What are the advantages of the MRF for depth completion (GMCN & GBP) in comparison with previous propagation-based methods? - The authors claim that " allowing the model to adaptively capture complex, long-range spatial dependencies guided by image content". Maybe it would be better if some cases are provided. - As shown in Tab. 9 of the Supplementary, the proposed method
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Taxonomy
TopicsAdvanced Vision and Imaging · Video Coding and Compression Technologies · Image Enhancement Techniques