ArXiv

Lossless-INR: Lossless Volumetric Implicit Neural Representations

2026-07-20 · 16:50 UTC ·Kaiyuan Tang, Daniel Burke, Chaoli Wang ·1 min read
Authors
Kaiyuan Tang, Daniel Burke, Chaoli Wang
Categories
cs.CV, cs.GR
arXiv
https://arxiv.org/abs/2607.18150v1
PDF
https://arxiv.org/pdf/2607.18150v1

Brief

Lossless-INR addresses lossiness in implicit neural representations for 3D scientific volumes by decomposing voxel values into binary bit-planes and posing reconstruction as per-bit binary classification. To keep exact recovery tractable and compact, the authors combine adaptive octree block partitioning with a ternary feature-grid network. The abstract reports zero bit-error, bit-exact reconstruction across datasets; only the abstract was available for this briefing.

Why it matters

Lossless-INR (Kaiyuan Tang, Daniel Burke, Chaoli Wang; arXiv:2607.18150v1, 2026-07-20) reformulates volumetric INR compression as bit-plane decomposition so reconstruction becomes per-bit binary classification, enabling exact voxel recovery if every bit is predicted correctly.

Key details

  • The method combines an octree block-partitioning that adaptively subdivides complex regions with a ternary feature-grid network whose grid entries are constrained to a ternary value set, trading optimization tractability for compactness.
  • Experiments report zero bit-error rate and bit-exact reconstruction on diverse 3D scientific volumetric datasets; code is available at https://github.com/TouKaienn/Lossless-INR and the paper was accepted as an IEEE VIS 2026 short paper.
Source evidence

Abstract

Implicit neural representation (INR) methods provide continuous coordinate-to-value mappings and integrate naturally with direct volume rendering, making them attractive for representing volumetric data. However, existing INR-based approaches for volumetric data are inherently lossy, and even small reconstruction errors can propagate through rendering and downstream analysis. In this work, we explore Lossless-INR, a lossless INR framework for 3D scientific volumetric data based on bit-plane decomposition. By decomposing each voxel value into binary bit-planes, we reformulate reconstruction as per-bit binary classification, so that exact recovery reduces to predicting every bit correctly. To make this optimization tractable while keeping the representation compact, we combine an octree block-partitioning strategy that adaptively subdivides complex regions with a ternary feature-grid network whose grid entries are parameterized by a ternary set of values. Experiments on diverse volumetric datasets show that this design can achieve zero bit-error rate and bit-exact reconstruction, enabling faithful rendering and downstream analysis with a compact representation. The code is available at https://github.com/TouKaienn/Lossless-INR.

Comment: Accepted in IEEE VIS 2026 (short paper)