$R^3$: 3D Reconstruction via Relative Regression

• URL: http://arxiv.org/abs/2605.26519v2
• Date: Thu, 28 May 2026 20:02:52 GMT
• Title: $R^3$: 3D Reconstruction via Relative Regression
• Abstract summary: $R3$ supports both full-context offline reconstruction and causal, bounded-memory streaming.<n>Our evaluation in both offline and streaming settings validates the effectiveness of our relative mechanism.
• Score: 45.709068651036795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent feed-forward geometry foundation models have demonstrated impressive generalization by recovering depth and poses in a single forward pass. However, these models are typically constrained by a global coordinate frame assumption. This dependency becomes a significant bottleneck for long-context and streaming reconstruction, as it forces the network to maintain an arbitrary temporal origin and handle translation magnitudes that grow unbounded over time. Our solution, which we call $R^3$, employs relative regression. We employ a lightweight MLP to predict confidence-weighted relative constraints. These confidences serve as a unified anchor: weighting losses during training and guiding pose aggregation during inference. $R^3$ supports both full-context offline reconstruction and causal, bounded-memory streaming. Our evaluation in both offline and streaming settings validates the effectiveness of our relative mechanism. Project page: https://kevinxu02.github.io/r3-site

Related papers

• Scal3R: Learning Efficient Multi-Relative Pose Query for Scalable Online 3D Reconstruction [43.823514226720846]
  Scal3R reformulates online reconstruction as multi-reference relative pose.<n>It reduces the average ATE by over 60% on KITTI compared to the online baseline.
  arXiv  Detail & Related papers  (2026-09-03T17:59:53Z)
• Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping [28.6268034980389]
  Long-horizon online visual mapping is a core capability for robot perception.<n>Recent feed-forward 3D reconstruction models predict poses in a fixed coordinate system tied to the first frame or a persistent scene memory.<n>We propose emphAnchor3R, a streaming 3D reconstruction framework that treats feed-forward reconstruction as current-centric local measurement prediction.
  arXiv  Detail & Related papers  (2026-06-03T16:00:13Z)
• BA-T: An Iterative Transformer for Two-View Bundle Adjustment [53.16990349592508]
  We propose BA-T, an iterative Transformer that implements BA-style structured updates as a repeatable layer in implicit token space.<n> experiments demonstrate that BA-T progressively improves pose and reconstruction accuracy across iterations.<n>BA-T provides a compact, efficient, and structural alternative to depth-heavy attention, enabling accurate 3D reconstruction within a lightweight architecture.
  arXiv  Detail & Related papers  (2026-06-02T07:51:14Z)
• Free Geometry: Refining 3D Reconstruction from Longer Versions of Itself [38.95274911975506]
  Free Geometry is a framework that enables feed-forward 3D reconstruction models to self-evolve at test time without any 3D ground truth.<n>Our approach consistently improves state-of-the-art foundation models, including Depth Anything 3 and VGGT, across 4 benchmark datasets.
  arXiv  Detail & Related papers  (2026-04-15T16:24:03Z)
• SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing [77.91660464664615]
  Diffusion Transformers (DiTs) have become a leading backbone for video generation, yet their quadratic attention cost remains a major bottleneck.<n>In this paper, we show that the missing contributions can be recovered without training.<n>We introduce SVG-EAR, a parameter-free linear compensation branch that uses the centroid to approximate skipped blocks and recover their contributions.
  arXiv  Detail & Related papers  (2026-03-09T22:15:31Z)
• TTT3R: 3D Reconstruction as Test-Time Training [69.51086319339662]
  We revisit the 3D reconstruction foundation models from a Test-Time Training perspective.<n>We leverage the alignment confidence between the memory state and incoming observations to derive a closed-form learning rate.<n>This training-free intervention, termed TTT3R, substantially improves length generalization.
  arXiv  Detail & Related papers  (2025-09-30T17:59:51Z)
• Test3R: Learning to Reconstruct 3D at Test Time [58.0912500917036]
  Test3R is a surprisingly simple test-time learning technique that significantly boosts geometric accuracy.<n>Our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks.
  arXiv  Detail & Related papers  (2025-06-16T17:56:22Z)
• DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation [6.327974053228352]
  We re-formulate 3D hand pose estimation as a dense ordinal regression problem. We propose a novel Dense Ordinal Regression 3D Pose Network (DOR3D-Net) Our design provides significant improvements over SOTA methods.
  arXiv  Detail & Related papers  (2024-03-20T08:47:51Z)
• Back to MLP: A Simple Baseline for Human Motion Prediction [59.18776744541904]
  This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. We show that the performance of these approaches can be surpassed by a light-weight and purely architectural architecture with only 0.14M parameters. An exhaustive evaluation on Human3.6M, AMASS and 3DPW datasets shows that our method, which we dub siMLPe, consistently outperforms all other approaches.
  arXiv  Detail & Related papers  (2022-07-04T16:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.