MeMix: Writing Less, Remembering More for Streaming 3D Reconstruction

Jiacheng Dong; Huan Li; Sicheng Zhou; Wenhao Hu; Weili Xu; Yan Wang

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arXiv · 2603.15330

MeMix: Writing Less, Remembering More for Streaming 3D Reconstruction

Jiacheng Dong·Huan Li·Sicheng Zhou·Wenhao Hu·Weili Xu·Yan Wang

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Abstract

Reconstruction is a fundamental task in 3D vision and a fundamental capability for spatial intelligence. Particularly, streaming 3D reconstruction is central to real-time spatial perception, yet existing recurrent online models often suffer from progressive degradation on long sequences due to state drift and forgetting, motivating inference-time remedies. We present MeMix, a training-free, plug-and-play module that improves streaming reconstruction by recasting the recurrent state into a Memory Mixture. MeMix partitions the state into multiple independent memory patches and updates only the least-aligned memory patches while exactly preserving others. This selective update mitigates catastrophic forgetting while retaining $O(1)$ inference memory, and requires no fine-tuning or additional learnable parameters, making it directly applicable to existing recurrent reconstruction models. Across standard benchmarks (ScanNet, 7-Scenes, KITTI, etc.), under identical backbones and inference settings, MeMix reduces reconstruction completeness error by 15.3% on average (up to 40.0%) across 300--500 frame streams on 7-Scenes. The code is available at https://dongjiacheng06.github.io/MeMix/

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BibTeX RIS

Jiacheng Dong, Huan Li, Sicheng Zhou, Wenhao Hu, Weili Xu, Yan Wang. 2026-03-16. MeMix: Writing Less, Remembering More for Streaming 3D Reconstruction. https://arxiv.org/abs/2603.15330

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