DMD-Net: Deep Mesh Denoising Network
Aalok Gangopadhyay, Shashikant Verma, Shanmuganathan Raman

TL;DR
DMD-Net introduces a novel deep learning framework utilizing graph convolutional networks and feature-guided transformers for effective mesh denoising, demonstrating robustness to high noise levels and outperforming existing methods.
Contribution
The paper presents a new end-to-end deep mesh denoising network combining primal-dual graph convolution and feature-guided transformers, with extensive ablation studies and state-of-the-art results.
Findings
Achieves superior denoising performance compared to existing algorithms.
Robust to various noise types and levels, including extremely high noise.
Components like the primal-dual fusion and feature-guided transformer are essential for optimal results.
Abstract
We present Deep Mesh Denoising Network (DMD-Net), an end-to-end deep learning framework, for solving the mesh denoising problem. DMD-Net consists of a Graph Convolutional Neural Network in which aggregation is performed in both the primal as well as the dual graph. This is realized in the form of an asymmetric two-stream network, which contains a primal-dual fusion block that enables communication between the primal-stream and the dual-stream. We develop a Feature Guided Transformer (FGT) paradigm, which consists of a feature extractor, a transformer, and a denoiser. The feature extractor estimates the local features, that guide the transformer to compute a transformation, which is applied to the noisy input mesh to obtain a useful intermediate representation. This is further processed by the denoiser to obtain the denoised mesh. Our network is trained on a large scale dataset of 3D…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
