SSR: A Training-Free Approach for Streaming 3D Reconstruction

• URL: http://arxiv.org/abs/2603.14765v1
• Date: Mon, 16 Mar 2026 02:55:14 GMT
• Title: SSR: A Training-Free Approach for Streaming 3D Reconstruction
• Abstract summary: Self-expressive Sequence Regularization (SSR) is a plug-and-play operator that enforces Grassmannian sequence regularity during inference.<n>We show that SSR consistently reduces drift and improves reconstruction quality across multiple streaming 3D reconstruction tasks.
• Score: 53.19807901585702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Streaming 3D reconstruction demands long-horizon state updates under strict latency constraints, yet stateful recurrent models often suffer from geometric drift as errors accumulate over time. We revisit this problem from a Grassmannian manifold perspective: the latent persistent state can be viewed as a subspace representation, i.e., a point evolving on a Grassmannian manifold, where temporal coherence implies the state trajectory should remain on (or near) this manifold.Based on this view, we propose Self-expressive Sequence Regularization (SSR), a plug-and-play, training-free operator that enforces Grassmannian sequence regularity during inference.Given a window of historical states, SSR computes an analytical affinity matrix via the self-expressive property and uses it to regularize the current update, effectively pulling noisy predictions back toward the manifold-consistent trajectory with minimal overhead. Experiments on long-sequence benchmarks demonstrate that SSR consistently reduces drift and improves reconstruction quality across multiple streaming 3D reconstruction tasks.

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