DefVINS is a visual‑inertial odometry pipeline tailored for deformable scenes, breaking the rigidity assumption of traditional VIO. It decomposes the odometry state into a rigid, IMU‑anchored component and a non‑rigid scene warp using an embedded deformation graph. The authors also introduce VIMandala, the first real‑world benchmark with ground‑truth camera poses for deformable VIO, and extend the synthetic Drunkard’s benchmark with inertial data, demonstrating that DefVINS outperforms both rigid and non‑rigid baselines.
arXiv:2611.07463v1 Announce Type: cross Abstract: LiDAR-Inertial-Visual Odometry (LIVO) integrates complementary LiDAR, camera, and IMU measurements to achieve robust state estimation. However, most...
DAVIO is a dense monocular‑inertial SLAM system that leverages a single multi‑view depth model (Depth Anything 3) for both initialization and mapping. It starts up quickly by solving a feature‑free linear system from a five‑image window and IMU pre‑integration, then uses a VIO filter whose metric poses condition the depth model during tracking. The system corrects residual scale along viewing rays, preserves metric baselines, and refines the map with a gravity‑preserving sub‑map graph, achieving earlier start‑up, lower localization error, and more accurate dense maps than state‑of‑the‑art feed‑forward mappers on both EuRoC and building‑scale ORI datasets.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
arXiv:2509.04600v2 Announce Type: replace Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
arXiv:2603.12064v3 Announce Type: replace Abstract: We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras -- a settin...
Accurate 4D reconstruction remains challenging: jointly estimating camera motion, scene geometry, and non-rigid deformation entails highly nonconvex optimization, making global optimality difficult to...
TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.
XM4D is a new framework for accurate 4D reconstruction that separates camera motion estimation from non‑rigid deformation. Both stages are cast as structured quadratically constrained quadratic programs (QCQPs) with semidefinite programming (SDP) relaxations, enabling certifiable global optimality. The method uses GPU‑accelerated Burer‑Monteiro solvers, initializes dynamic Gaussian splatting for refinement, and achieves state‑of‑the‑art performance on seven benchmarks.
arXiv:2603.17068v3 Announce Type: replace Abstract: Structured 3D representations such as keypoints and meshes offer compact, expressive descriptions of deformable objects, jointly capturing geometri...
The paper introduces a reliability-regulated trajectory optimization framework for progressive COLMAP‑free 3D Gaussian Splatting (3DGS). It uses a self‑supervised bidirectional cycle‑consistency mechanism to control camera trajectory estimation through forward motion propagation and retrospective trajectory correction, thereby reducing error compounding without external priors. Experiments on Tanks and Temples and CO3D‑V2 demonstrate improved camera trajectory accuracy and novel‑view rendering quality compared to existing unposed baselines.
arXiv:2609.40244v1 Announce Type: new Abstract: Mobile robots and vehicles carry synchronized multi-camera rigs, yet many streaming 3D foundation models are designed for monocular input, leaving effi...
The paper introduces SLIM-init, a monocular visual‑inertial initialization method that avoids explicit 3D reconstruction by using 2D line features and vanishing points. It provides robust rotation constraints through line‑derived vanishing points and improves translation estimation with a line epipolar residual and a line‑normal projection residual. Experiments on public benchmarks and custom degenerate‑motion sequences show that SLIM-init achieves higher accuracy and robustness than existing initialization techniques.