SPARK: Scalable Real-Time Point Cloud Aggregation with Multi-View Self-Calibration

• URL: http://arxiv.org/abs/2601.08414v2
• Date: Thu, 15 Jan 2026 01:43:02 GMT
• Title: SPARK: Scalable Real-Time Point Cloud Aggregation with Multi-View Self-Calibration
• Abstract summary: SPARK is a self-calibrating real-time multi-camera point cloud reconstruction framework.<n>It produces stable point clouds in dynamic scenes while scaling linearly with the number of cameras.
• Score: 1.3537117504260623
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Real-time multi-camera 3D reconstruction is crucial for 3D perception, immersive interaction, and robotics. Existing methods struggle with multi-view fusion, camera extrinsic uncertainty, and scalability for large camera setups. We propose SPARK, a self-calibrating real-time multi-camera point cloud reconstruction framework that jointly handles point cloud fusion and extrinsic uncertainty. SPARK consists of: (1) a geometry-aware online extrinsic estimation module leveraging multi-view priors and enforcing cross-view and temporal consistency for stable self-calibration, and (2) a confidence-driven point cloud fusion strategy modeling depth reliability and visibility at pixel and point levels to suppress noise and view-dependent inconsistencies. By performing frame-wise fusion without accumulation, SPARK produces stable point clouds in dynamic scenes while scaling linearly with the number of cameras. Extensive experiments on real-world multi-camera systems show that SPARK outperforms existing approaches in extrinsic accuracy, geometric consistency, temporal stability, and real-time performance, demonstrating its effectiveness and scalability for large-scale multi-camera 3D reconstruction.

Related papers

• FFVO: A Feedforward Pose Decoder for Long-Horizon Visual Odometry [31.83986431141471]
  We propose Feedforward Visual Odometry (FFVO), a pose-specialized adaptation of joint reconstruction architectures for efficient and temporally stable camera-pose estimation.<n>FFVO uses (i) a compact camera-token representation for computationally efficient temporal aggregation, (ii) a hierarchical local-to-global temporal decoder, and (iii) intermediate trajectory supervision that promotes temporal stability.
  arXiv  Detail & Related papers  (2026-09-12T05:56:52Z)
• OmniPoint: Universal Monocular Metric Pointcloud from Any Camera [79.24417597712802]
  We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors.<n>To overcome projection rigidity, OmniPoint abandons conventional planar depth regression.<n>It instead adopts a decoupled ray and distance representation alongside a decoupled training objective.
  arXiv  Detail & Related papers  (2026-09-08T19:45:58Z)
• Sphere-VIO: Fast and Robust Visual-Inertial Odometry via Unified Spherical Representation for Heterogeneous Multi-Camera Systems [11.777651307702174]
  Multi-camera visual-inertial odometry (VIO) overcomes the inherent limitations of pure visual systems by expanding the field of view.<n>We present Sphere-VIO, a lightweight filter-based VIO framework with unified spherical representation for heterogeneous multi-camera systems.<n>We show that Sphere-VIO achieves superior trade-offs between accuracy, robustness, efficiency, and cross-camera generality.
  arXiv  Detail & Related papers  (2026-06-29T07:46:08Z)
• High-Resolution Single-Shot Polarimetric Imaging Made Easy [76.04914313843774]
  EasyPolar is a multi-view polarimetric imaging framework.<n>Three synchronized RGB cameras capture one unpolarized view and two polarized views with distinct orientations.<n>A confidence-guided polarization reconstruction network is proposed to address the potential misalignment in multi-view fusion.
  arXiv  Detail & Related papers  (2026-04-07T08:20:58Z)
• VS3R: Robust Full-frame Video Stabilization via Deep 3D Reconstruction [8.43362854806854]
  We propose VS3R, a framework that synergizes feed-forward 3D reconstruction with generative video diffusion.<n>Our pipeline jointly estimates camera parameters, depth, and masks to ensure all-scenario reliability.<n>A Dual-Stream Video Diffusion Model restores disoccluded regions and rectifies artifacts by synergizing structural guidance with semantic anchors.
  arXiv  Detail & Related papers  (2026-03-06T03:15:06Z)
• DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation [72.89376712495464]
  DAGE is a dual-stream transformer that disentangles global coherence from fine detail.<n>A low-resolution stream operates on aggressively downsampled frames with alternating frame/global attention to build a view-consistent representation.<n>A high-resolution stream processes the original images per-frame to preserve sharp boundaries and small structures.<n>This design scales resolution and clip length independently, supports inputs up to 2K, and maintains practical inference cost.
  arXiv  Detail & Related papers  (2026-03-04T05:29:29Z)
• MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detection [6.350460753267439]
  MambaFusion is a unified multi-modal detection framework that achieves efficient, adaptive, and physically grounded 3D perception.<n>A structure-conditioned diffusion head integrates graph-based reasoning with uncertainty-aware denoising, enforcing physical plausibility, and calibrated confidence.<n>The framework demonstrates that coupling SSM-based efficiency with reliability-driven fusion yields robust, temporally stable, and interpretable 3D perception for real-world autonomous driving systems.
  arXiv  Detail & Related papers  (2026-02-08T21:10:46Z)
• Depth-Guided Metric-Aware Temporal Consistency for Monocular Video Human Mesh Recovery [28.9705779052755]
  We propose a comprehensive framework that achieves metric-aware temporal consistency through three synergistic components.<n>A Depth-Guided Multi-Scale Fusion module adaptively integrates geometric priors with RGB features via confidence-aware gating.<n>A Motion-Depth Aligned Refinement (MoDAR) module that enforces temporal coherence through cross-modal attention between motion dynamics and geometric cues.
  arXiv  Detail & Related papers  (2026-02-04T06:41:03Z)
• FUSE-Flow: Scalable Real-Time Multi-View Point Cloud Reconstruction Using Confidence [1.3537117504260623]
  Real-time multi-view point cloud reconstruction is a core problem in 3D vision and immersive perception.<n>We propose FUSE-Flow, a frame-wise, stateless, and linearly scalable point cloud streaming reconstruction framework.<n>Experiments demonstrate that the framework improves reconstruction stability and geometric fidelity in overlapping, depth-discontinuous, and dynamic scenes.
  arXiv  Detail & Related papers  (2026-02-01T05:45:27Z)
• Temporal-Anchor3DLane: Enhanced 3D Lane Detection with Multi-Task Losses and LSTM Fusion [0.0]
  Temporal-Anchor3DLane is an enhanced 3D lane detection framework that extends Anchor3DLane with three key contributions.<n>A lightweight Temporal LSTM Fusion module aggregates per-anchor features across frames, replacing a heavier Transformer-style temporal fusion.<n>On OpenLane, Temporal-Anchor3DLane improves F1 by +6.2 and yields smoother temporal trajectories.
  arXiv  Detail & Related papers  (2025-12-05T21:52:18Z)
• DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion [28.146811420532455]
  We introduce DVLO4D, a novel visual-LiDAR odometry framework that leverages sparse spatial-temporal fusion to enhance accuracy and robustness.<n>Our method has high efficiency, with an inference time of 82 ms, possessing the potential for the real-time deployment.
  arXiv  Detail & Related papers  (2025-09-07T11:43:11Z)
• A Continuous-Time Consistency Model for 3D Point Cloud Generation [0.6308539010172308]
  We introduce ConTiCoM-3D, a continuous-time consistency model that synthesizes 3D directly in point space.<n>The method integrates a TrigFlow-inspired continuous noise schedule with a Chamfer Distance-based geometric loss.<n> Experiments on the ShapeNet benchmark show that ConTiCoM-3D matches or outperforms state-of-the-art diffusion and latent consistency models in both quality and efficiency.
  arXiv  Detail & Related papers  (2025-09-01T14:11:59Z)
• GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering [54.489285024494855]
  Video stabilization is pivotal for video processing, as it removes unwanted shakiness while preserving the original user motion intent.<n>Existing approaches, depending on the domain they operate, suffer from several issues that degrade the user experience.<n>We introduce textbfGaVS, a novel 3D-grounded approach that reformulates video stabilization as a temporally-consistent local reconstruction and rendering' paradigm.
  arXiv  Detail & Related papers  (2025-06-30T15:24:27Z)
• Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration [42.67358714902724]
  DM-Calib is a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image.<n>We introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics.<n>By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation.
  arXiv  Detail & Related papers  (2024-11-26T09:04:37Z)
• Dynamic 3D Point Cloud Sequences as 2D Videos [81.46246338686478]
  3D point cloud sequences serve as one of the most common and practical representation modalities of real-world environments. We propose a novel generic representation called textitStructured Point Cloud Videos (SPCVs) SPCVs re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points.
  arXiv  Detail & Related papers  (2024-03-02T08:18:57Z)
• Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction [54.00007868515432]
  Existing methods face challenges in estimating the accurate correction field due to the uniform velocity assumption. We propose a geometry-based Quadratic Rolling Shutter (QRS) motion solver, which precisely estimates the high-order correction field of individual pixels. Our method surpasses the state-of-the-art by +4.98, +0.77, and +4.33 of PSNR on Carla-RS, Fastec-RS, and BS-RSC datasets, respectively.
  arXiv  Detail & Related papers  (2023-03-31T15:09:18Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• Self-Supervised Multi-Frame Monocular Scene Flow [61.588808225321735]
  We introduce a multi-frame monocular scene flow network based on self-supervised learning. We observe state-of-the-art accuracy among monocular scene flow methods based on self-supervised learning.
  arXiv  Detail & Related papers  (2021-05-05T17:49:55Z)
• Estimating Egocentric 3D Human Pose in Global Space [70.7272154474722]
  We present a new method for egocentric global 3D body pose estimation using a single-mounted fisheye camera. Our approach outperforms state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2021-04-27T20:01:57Z)
• CoMo: A novel co-moving 3D camera system [0.0]
  CoMo is a co-moving camera system of two synchronized high speed cameras coupled with rotational stages. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system "home" configuration. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%.
  arXiv  Detail & Related papers  (2021-01-26T13:29:13Z)

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.