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Neural Compression of 3D Meshes using Sparse Implicit Representation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
compression3D mesh
Abstract:

The growing demand for high-quality 3D mesh models has fueled the need for efficient 3D mesh compression techniques. However, existing methods often exhibit suboptimal compression performance due to the inefficient representation of mesh data. To address this issue, we propose a novel neural mesh compression method based on Sparse Implicit Representation (SIR). Specifically, SIR records signed distance field (SDF) values only on regular grids near the surface, enabling high-resolution structured representation of arbitrary geometric data with a significantly lower memory cost, while still supporting precise surface recovery. Building on this representation, we construct a lightweight Sparse Neural Compression (SNC) network to extract compact embedded features from the SIR and encode them into a bitstream. Extensive experiments and ablation studies demonstrate that our method outperforms state-of-the-art mesh and point cloud compression approaches in both compression performance and computational efficiency across a variety of mesh models. The code is included in the Supplementary Material.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a neural mesh compression method using Sparse Implicit Representation (SIR), which records signed distance field values only on regular grids near surfaces. Within the taxonomy, it occupies the 'Sparse Implicit Representation-Based Compression' leaf under 'Neural Compression Frameworks and Encoding Strategies'. Notably, this leaf contains only the original paper itself—no sibling papers are present—indicating this is a relatively sparse research direction within the broader neural compression landscape, which encompasses fourteen papers across multiple branches.

The taxonomy reveals neighboring work in sibling leaves: 'Lossless and Distribution-Agnostic Implicit Compression' and 'Adaptive and Feature-Aware Implicit Compression' both address neural compression frameworks but differ in their core strategies. The parent branch 'Implicit Neural Representation Architectures for 3D Data' contains hierarchical, displacement-based, and weight-encoded approaches that focus on representation design rather than compression pipelines. The paper's sparse grid strategy connects conceptually to hierarchical methods like octree-based representations, yet diverges by targeting compression efficiency rather than pure architectural innovation.

Among twenty-three candidates examined, the Sparse Implicit Representation contribution shows overlap: ten candidates were reviewed, with two appearing to provide refutable prior work. The Sparse Neural Compression network examined three candidates with none refuting, suggesting greater novelty in the encoding architecture. Variable-rate compression via resolution-agnostic inference examined ten candidates with no refutations, indicating this aspect may be less explored in the limited search scope. The statistics suggest the core SIR concept has more substantial prior work than the network design or variable-rate mechanism.

Based on the top-twenty-three semantic matches examined, the work appears to occupy a relatively underexplored niche within neural mesh compression, though the sparse implicit representation concept itself has some precedent. The analysis covers a focused subset of the literature and does not claim exhaustive coverage; broader searches or domain-specific venues might reveal additional related work, particularly in mesh-specific compression or adaptive sampling strategies.

Taxonomy

Core-task Taxonomy Papers
14
3
Claimed Contributions
23
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: neural compression of 3D meshes using sparse implicit representation. The field organizes around several complementary directions. Implicit Neural Representation Architectures for 3D Data explores foundational network designs that encode geometry as continuous functions, while Neural Compression Frameworks and Encoding Strategies focuses on how to efficiently parameterize and transmit these representations—often leveraging sparsity, quantization, or learned encodings. Point Cloud and Geometry Set Compression addresses discrete geometric primitives, Scene-Level and Radiance Field Compression targets view synthesis and volumetric rendering, and Dimensionality Reduction and Spatio-Temporal Compression tackles dynamic or high-dimensional data. Representative works such as SHINE Mapping[1] and Adaptive Volumetric INR[2] illustrate adaptive spatial partitioning, while Quantized Neural Displacement[3] and Tinc[7] demonstrate quantization-driven compression strategies. Across these branches, the central tension is balancing reconstruction fidelity against compactness and computational cost. Recent efforts highlight diverse trade-offs: some methods prioritize extreme sparsity by encoding only occupied regions or salient features, as seen in LINR Point Cloud[5] and Implicit Point Compression[6], whereas others like Geometry Sets Compression[9] and NeCGS[13] emphasize structured representations for complex scenes. Sparse Implicit Meshes[0] sits naturally within the Neural Compression Frameworks branch, sharing the sparsity-driven philosophy of works like Quantized Neural Displacement[3] and Hierarchical Neural Surfaces[11], yet it distinguishes itself by targeting mesh-specific topology rather than volumetric grids or point sets. Compared to Adaptive Volumetric INR[2], which adapts resolution spatially, and Visual Representation Compression[10], which addresses broader visual data, Sparse Implicit Meshes[0] focuses on exploiting mesh connectivity to achieve compact implicit encodings. This positioning underscores an ongoing exploration of how domain-specific structure—whether mesh, point cloud, or radiance field—can inform more efficient neural compression schemes.

Claimed Contributions

Sparse Implicit Representation (SIR) for 3D meshes

The authors introduce a sparse implicit representation that stores SDF values only on grids near the mesh surface rather than densely throughout space. This enables high-resolution structured representation of arbitrary geometry with significantly lower memory cost while supporting precise surface recovery via an adapted Marching Cubes algorithm.

10 retrieved papers
Can Refute
Sparse Neural Compression (SNC) network

The authors develop a lightweight sparse convolutional autoencoder network that compresses the sparse SDF tensors into compact latent features through downscaling blocks, which are then quantized and entropy-coded into a bitstream. The network is trained end-to-end with rate-distortion optimization.

3 retrieved papers
Variable-rate compression via resolution-agnostic inference

The authors propose a variable-rate compression approach where a single trained model can be applied to different input resolutions to achieve coarse rate control, with fine-grained adjustment via models trained with different rate-distortion trade-off parameters. This provides efficient rate adaptation without model retraining.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Sparse Implicit Representation (SIR) for 3D meshes

The authors introduce a sparse implicit representation that stores SDF values only on grids near the mesh surface rather than densely throughout space. This enables high-resolution structured representation of arbitrary geometry with significantly lower memory cost while supporting precise surface recovery via an adapted Marching Cubes algorithm.

Contribution

Sparse Neural Compression (SNC) network

The authors develop a lightweight sparse convolutional autoencoder network that compresses the sparse SDF tensors into compact latent features through downscaling blocks, which are then quantized and entropy-coded into a bitstream. The network is trained end-to-end with rate-distortion optimization.

Contribution

Variable-rate compression via resolution-agnostic inference

The authors propose a variable-rate compression approach where a single trained model can be applied to different input resolutions to achieve coarse rate control, with fine-grained adjustment via models trained with different rate-distortion trade-off parameters. This provides efficient rate adaptation without model retraining.

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