Pose_Estimation
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Camera-to-Quadruped Robot Pose Estimation from Sequential Images
2026-10-27 03:59:43Seobin Hwang, Ji-Seong Beom, Yeong-Jun ChoAbstract
Quadruped robots are increasingly deployed in hazardous environments such as disaster response and facility inspection, where external-camera monitoring is essential for safe operation. This setting is challenging because quadrupeds are articulated systems with time-varying leg joints, leg symmetry, and frequent self-occlusions, which make keypoint correspondence ambiguous. Moreover, no public benchmark exists, making quantitative comparison difficult. To address these challenges, we propose QuadPose, which estimates the full-body 18-DoF pose (6-DoF base pose + 12-DoF joint angles) from monocular video sequences, and introduce QuadSyn, the first camera-to-quadruped robot pose benchmark. On QuadSyn, QuadPose achieves 92.18% PCK@2.5px, 95.54% PCK-AUC, 85.94% ADD@20cm, 66.98% ADD-AUC, and 3.65 degree mean joint-angle error at approximately 33 FPS.
Abstract (translated)
URL
https://arxiv.org/abs/2610.36599
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Learning Scene Coordinate Reconstruction from Unposed Images via Pose Graph Optimization
2026-10-26 14:29:13Tze Ho Elden Tse, Jizong Peng, Angela YaoAbstract
Learning-based structure-from-motion methods such as ACE-Zero have demonstrated strong performance in estimating cameraposes and scene coordinates from unordered image collections without requiring ground truth supervision. However, the lack of global and multi-view consistency constraints in ACE-Zero can lead to pose drift and misalignment, particularly in complex or ambiguous scenes. In this work, we propose a hybrid framework that integrates pose graph optimization (PGO) into ACE-Zero to refine camera poses and suppress incorrect refinements. We construct pose graphs directly from ACE-Zero outputs by extracting relative pose constraints from predicted scene coordinates. Furthermore, we introduce an uncertainty-aware optimization strategy by estimating confidence scores using geometric priors, including epipolar and optical flow consistencies across views. Our approach improves the robustness and accuracy of pose estimation, demonstrating that global geometric reasoning can effectively complement learning-based inference in structure-from-motion.
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URL
https://arxiv.org/abs/2610.35501
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SG-Splat: Stable Geometry Learning under Pose Uncertainty for Pose-Free 3D Gaussian Splatting
2026-10-26 12:50:21Xiaohan Lu, Jiaqi Zhang, Weijia Jiang, Siwei MaAbstract
3D Gaussian Splatting (3DGS) has become a promising representation for real-time novel view synthesis and 3D scene reconstruction. For practical deployment, recent studies have explored pose-free feed-forward 3DGS, which reconstructs Gaussian representations directly from unposed input images. However, these methods remain sensitive to residual pose errors, which perturb canonical-space Gaussian transformation, introducing noisy supervision for geometry and attribute learning. To address this problem, we present SG-Splat, a pose-free feed-forward 3DGS framework that improves robustness to pose uncertainty and stabilizes the learning of Gaussian primitives. SG-Splat decouples geometry estimation and Gaussian attribute prediction into two dedicated branches, reducing error coupling between pose-sensitive geometry and Gaussian attributes. We further introduce a coarse-to-fine refinement pipeline, consisting of Flow-Guided Pose Refinement (FGPR) and Residual-Guided Geometry Correction (RGC), to improve geometric alignment before Gaussian attribute reconstruction. Specifically, FGPR refines camera poses with flow-based correlation features and differentiable bundle adjustment. RGC then corrects initial point maps using depth residuals derived from FGPR, producing more reliable Gaussian centers. To stabilize training, we introduce a dual-path strategy that mixes ground-truth and predicted poses for canonical transformation. It provides clean supervision for Gaussian attributes while improving geometry robustness to residual pose errors. Extensive experiments on DL3DV and RealEstate10K show that SG-Splat achieves state-of-the-art performance in both novel view synthesis and pose estimation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.35361
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PosePlaner: Denoising Any Feed-Forward Pose Predictor from Pairwise Planar Geometry
2026-10-26 04:55:36Yihang Chen, Bin Tan, Yujun Shen, Yinghao Xu, Nan XueAbstract
Recent multi-view Transformers (e.g., DUSt3R and VGGT) have advanced 3D reconstruction by scaling training data and model capacity from two views to many views and long sequences, subsuming the classical SfM/SLAM pipeline of feature extraction, matching, and pose estimation in a single feed-forward pass. Yet this subsumption hides the interfaces between pipeline stages---correspondences, geometry, and pose are entangled in a single forward pass, leaving supervised fine-tuning as the major path to improvement and offering no mechanism to correct predictions from geometric evidence at test time. We propose PosePlaner to close this gap by reviving the classical coupling between correspondences and pose via flow matching, treating pose refinement as a conditional generation problem that takes coarse geometric matches and initial pose as input condition and fuses the resulting pose hypotheses into a single refined estimate, providing a continuous, data-driven analog of RANSAC voting. Our method is trained efficiently on two-view data, yet serves as a periodic refiner for streaming n-view pose estimators with no retraining. Empirically, it corrects failures of feed-forward predictors, substantially improves correspondence-based solvers, and reduces accumulated drift in long streaming sequences.
Abstract (translated)
URL
https://arxiv.org/abs/2610.34845
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Selective Monocular Precession Estimation under Geometric and Evaluation Ambiguity: An Auditable Synthetic Study with ORBIT-SCOUT
2026-10-26 04:04:02Carlos Eduardo SanojaAbstract
Monocular observations of a tumbling object can support a precise fitted trajectory without establishing that the recovered physical axis is correct. We study this distinction for elongated, approximately cylindrical targets observed by a calibrated stationary camera. ORBIT-SCOUT couples image-derived line and rim geometry to constant-cone and torque-free triaxial trajectory families, then returns a linked momentum-axis and mean-precession-rate estimate only when numerical, geometric, temporal and competing-hypothesis checks pass. We describe the measurement objective, selection policy and limitations, and situate them within spacecraft pose estimation, unknown-target reconstruction, rigid-body identification and selective prediction. An internally preregistered population of 96 new synthetic scenes was processed in two cold passes, retaining all 192 attempts. In the primary pass, 38 scenes produced jointly correct estimates, none produced an incorrect estimate, 57 were withheld and one execution was incomplete. Correct coverage on the 64 supported scenes was 25/64. Ninety-five repeated pairs were scientifically equivalent under a frozen comparison policy; the remaining pair was incomplete. The registered coverage, failure-free execution and all-case repeatability targets were not met. Historical audits also exposed an overly permissive axis metric and motion-dependent acquisition cadence. The contribution is a documented measurement-to-decision implementation and a failure-preserving evaluation case study, not a new rigid-body theory or evidence of state-of-the-art accuracy. Zero observed errors does not establish calibrated risk, and conditional temporal checks do not remove shared image-feature bias. Real-image transfer and external reproduction remain open.
Abstract (translated)
URL
https://arxiv.org/abs/2610.34819
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StickFigure: Enabling Pose-Only IoT Cameras for Privacy-Preserving Edge Vision
2026-10-25 07:58:48Aveline Xiaoyuan Han, Chenshu WuAbstract
Indoor IoT cameras can support assisted living and activity-aware spaces, but exporting raw RGB frames raises privacy concerns by exposing facial, clothing, and household details that body-motion tasks often do not require. We present StickFigure, a pose-only IoT camera system that, during real-time monitoring, keeps raw frames local and ephemeral while exporting only compact skeleton streams sufficient for downstream human activity recognition (HAR). While pose estimation has matured on GPUs, extracting accurate pose streams on resource-constrained IoT cameras remains difficult. To overcome these challenges and enable on-device pose streams on (almost) any IoT camera, we present two core designs in StickFigure: an activation-aware pose backbone that allocates channel capacity after spatial downsampling and a lightweight hierarchical MLP refiner that selectively corrects elbow and wrist coordinates using intermediate visual features and kinematic context. We fully implement StickFigure and evaluate it on public benchmarks and real-world deployment. Compared with state-of-the-art MCU pose models, StickFigure achieves the highest pose accuracy and downstream HAR utility on both NTU RGB+D and Penn Action, while using 96.5% fewer parameters and 99.3% fewer MACs per frame than RTMPose-S-based baselines, achieving 0.107 s p95 latency on a representative STM32H753 MCU. We believe StickFigure establishes a new operating point for privacy-aware edge vision on massive IoT cameras.
Abstract (translated)
URL
https://arxiv.org/abs/2610.34130
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Bracketing Shows the Way: Adaptive Multi-Exposure Policy as Input for Robust Visual SLAM
2026-10-23 18:16:24Olivier Gamache, Anthony Lavertu, David-Alexandre Duclos, Jean-Michel Fortin, Fran\c{c}ois Pomerleau, Philippe Gigu\`ereAbstract
Rapid illumination transitions can disturb Visual SLAM (VSLAM) by saturating images before the Automatic Exposure (AE) policy adapts, removing the visual correspondences needed to maintain tracking. Our proposed methods mitigate this disturbance at two levels: B-Adapt acquires adaptive exposure brackets to cover the scene dynamic range while promoting photometric overlap, and BVSLAM directly uses the multi-exposure sequence as constraints in a factor graph state estimator without relying on High Dynamic Range (HDR) merging. We evaluate both methods against state-of-the-art approaches in nominal environments and during a repeated tunnel-exit transition at an emulated vehicle speed of 32 m s-1. In the tunnel, combining our B-Adapt with BVSLAM reduces the state estimation error by 78 % compared with AE images. These results suggest that bracketing efficiently improves visual pose estimation robustness to rapid illumination disturbances while retaining low error under nominal conditions. Our code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33174
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FlexGlue: Sparse-to-Dense Feature Matching and Visual Disambiguation
2026-10-23 17:59:00Philipp Lindenberger, Paul-Edouard Sarlin, Ondrej Miksik, Marc PollefeysAbstract
To estimate correspondences across images, existing approaches match either sparse interest points, which is highly efficient but limited by repeatability, or dense pixel grids, which is robust but expensive. Furthermore, these approaches are trained with only matching pairs and thus often fail to reject unrelated pairs that exhibit visual symmetries, which is equally important in applications like Structure-from-Motion (SfM). We bridge these gaps by introducing FlexGlue, a neural network for asymmetric sparse-to-dense matching and disambiguation. Given two images and coordinates of points in the first image, FlexGlue estimates the coordinates of the corresponding points in the second image while simultaneously rejecting the pair if non-matching. By searching for a limited set of correspondences among dense pixels, our approach is both fast and effective at both matching and disambiguation: it combines the sub-pixel accuracy of dense matching with the speed of sparse matching, while rejecting incorrect pairs with high precision. Experiments on multiple datasets show that FlexGlue pushes the Pareto frontier in relative pose estimation and significantly outperforms existing approaches for SfM and visual localization. FlexGlue is publicly available at this https URL .
Abstract (translated)
URL
https://arxiv.org/abs/2610.32976
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Resilient Decentralized Radar-LiDAR-Inertial Odometry in Challenging Subterranean Environments
2026-10-23 15:58:05Moumita Mukherjee, Avijit Banerjee, Anton Koval, George NikolakopoulosAbstract
Accurate localization in GPS-denied and visually degraded environments affected by smoke, dust, or fog remains a critical challenge for autonomous systems. While LiDAR provides high-resolution spatial perception, its performance deteriorates under particulate occlusion. In contrast, frequency-modulated continuous-wave (FMCW) radar offers robustness under such conditions but suffers from sparse measurements and outliers. This article presents a resilient decentralized LiDAR Radar Inertial Odometry (LRIO) framework that fuses the complementary strengths of LiDAR, radar, and inertial sensing to achieve robust pose estimation in challenging subterranean environments. At the core of the framework is an Adaptive Residual Thresholding (ART) mechanism that autonomously detects temporal sensor degradation and selectively adjusts the fusion process to minimize unreliable measurements. Building on this, a Robust Error-Tolerant Optimal Information Filter (RETOIF) is employed within a decentralized fusion architecture to integrate LiDAR-inertial and multiple radar-inertial odometry estimates in a fault-aware manner, enhancing resilience to environmental disturbances and intermittent sensor failures. The proposed framework is evaluated using real-world sensor data collected in GPS-denied subterranean tunnels under smoke-free and smoke-degraded conditions. In the smoke-free experiment, the proposed RETOIF framework achieves a root mean square localization error (RMSE) of 2.1733, compared with 2.9929 for the conventional centralized LRIO approach, corresponding to a 27.4\% reduction in RMSE. Experimental results further demonstrate the framework's ability to maintain pose estimation under smoke-induced sensor degradation, highlighting its potential for resilient autonomous navigation and mapping in challenging subterranean environments.
Abstract (translated)
URL
https://arxiv.org/abs/2610.35363
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CompMVR: Compression-Aware Multi-View Restoration Using Diffusion Models for Geometrically Consistent 3D Reconstruction
2026-10-23 15:40:18Dong-hwi Kim, Chaewon Moon, Hojun Song, Dongbeom Kim, Junyeong Jang, Aro Kim, Gahyeon Kim, Heejung Choi, Jehee Kim, Gianella Cravioto, Sohyun Lee, Gyeongjin Choi, EunHye Jeong, Soo Ye Kim, Jaehyup Lee, Sang-hyo ParkAbstract
Real-world 3D reconstruction pipelines often rely on multi-view images degraded by lossy compression. While compression artifacts have been extensively studied in single-image or video restoration, their impact on multi-view geometry remains largely overlooked. In this work, we identify compression-induced cross-view geometric inconsistency as a critical failure mode, where view-dependent artifacts disrupt feature matching, dense correspondence, camera parameter estimation, and downstream 3D reconstruction tasks such as novel view synthesis. To address this problem, we propose CompMVR, a compression-aware multi-view diffusion restoration framework that jointly refines compressed views while preserving cross-view consistency, and construct compressed datasets for systematic evaluation. Unlike conventional restoration methods that optimize each image independently for 2D perceptual quality, CompMVR leverages learned compression priors and cross-view correspondence to recover visual details while improving geometric reliability across views. Experiments across diverse datasets, codecs, and compression levels demonstrate gains in restoration quality and downstream 3D tasks, including novel-view synthesis, camera pose estimation, and view matching. These results establish compressed multi-view restoration as a distinct problem and highlight its importance for 3D reconstruction under lossy compression. Code and data for this paper are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.32800
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VERA-Hand: A Verified Multidimensional Benchmark for 2D Hand Pose Estimation in Third-Person Dynamic Gesture Videos
2026-10-23 11:44:54Gibran Benitez-Garcia, Hiroki TakahashiAbstract
Hand landmarks serve as input for gesture recognition, sign language understanding, and touchless human-computer interaction. Yet hand pose estimators are typically evaluated with frame-wise localization metrics such as PCK or MPJPE, which are computed over matched predictions and overlook missed or spurious hands, handedness errors, and temporal instability. To address these limitations, we present VERA-Hand, a benchmark that evaluates estimators using manually verified, frame-level 2D annotations for 10,690 hands across four complementary dimensions (detection, localization, identification, and temporal stability), as well as a joint Pose-F1 metric that requires both correct detection and accurate localization. Evaluating six estimators spanning hand-specific, whole-body, reconstruction-based, and foundation-model paradigms, we find that no estimator dominates: rankings reorder sharply across dimensions. VERA-Hand also exposes failures that localization-only and downstream evaluations may not reveal, including cross-hand overlap in over 85% of frames for a recent foundation model. A post-hoc cleanup case study demonstrates how the benchmark can validate corrective interventions, while stratified analyses show that robustness varies across motion-blur classes and hand regions. We release the verified annotations, protocol, and code at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2610.32517
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F2G-Pose: Geometry-Aware Foundation Feature Lifting for Direct RGB-D Category-Level Object Pose Estimation
2026-10-22 17:43:34Jinyu Zhang, Haitao Lin, Jiashu Hou, Xiangyang Xue, Yanwei FuAbstract
This paper studies category-level RGB-D object pose estimation, which recovers an object's 3D rotation and translation without instance-specific CAD models or reference views. This task is highly ambiguous due to intra-class variations, symmetries, and partial observations. Current solutions struggle to balance reliability and efficiency: methods based on intermediate structures are prone to error propagation, while diffusion models suffer from high inference latency and complex post-hoc candidate extraction. To address this, we present F2G-Pose, a real-time direct, single-pass framework that predicts category-level pose, metric size, and camera-frame completed shape from a single RGB-D observation in one feed-forward pass. F2G-Pose lifts dense visual foundation features to partial point clouds, encodes local geometry with DGCNN tokenization, and integrates object-level structure with a Mixture-of-Experts Transformer. Joint size prediction and camera-frame shape completion encourage metric consistency through dense geometric supervision. With its task-specific network trained only on pose-annotated synthetic SOPE data and its frozen RADIO backbone externally pretrained, F2G-Pose achieves state-of-the-art accuracy on SOPE and ROPE while running at 28 FPS for pose-only inference, and further demonstrates strong camera-frame geometry on HouseCat6D and real-object transfer on HANDAL. Analyses of latency, robustness, shape completion, and GenPose++ candidate behavior further demonstrate the efficiency and reliability of F2G-Pose, with real-world manipulation experiments demonstrating deployment potential under segmented RGB-D inputs. The project page, code, and pretrained models are made available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.31623
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Stream4D: Streaming 4D Reconstruction with Training-free Motion Decoupling via Causal KV-Cache Attention
2026-10-22 14:11:34Wei Zhang, Ying Feng, Mengtong Wu, Shiqiang Gong, Huaqi Zhang, Qi ZhangAbstract
4D scene reconstruction requires robust handling of dynamic objects, yet existing streaming methods commonly assume static scenes, leading to pose drift and ghosting artifacts over long sequences. We introduce Stream4D, a training-free, motion-decoupled streaming reconstruction framework that can be directly integrated into causal Transformer backbones (e.g., StreamVGGT, Stream3R). Our key observation is that causal attention implicitly encodes rich temporal motion cues within the KV cache. We adaptively aggregate these cues from multiple attention layers and across temporal windows, and fuse them with depth priors to handle challenging cases such as occlusions and textureless regions, producing precise dynamic masks that decouple static and dynamic regions. We then apply a soft key masking scheme that leverages these dynamic masks to suppress interference from dynamic regions in causal attention, thereby improving camera pose estimation accuracy. Stream4D inherits the efficiency of causal attention and remains compatible with existing streaming architectures. On diverse dynamic-scene benchmarks, Stream4D significantly improves camera pose estimation and static-scene reconstruction quality, yields reliable dynamic masks, and effectively mitigates long-sequence ghosting.
Abstract (translated)
URL
https://arxiv.org/abs/2610.31320
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Uncovering the Blind Spots of OKS-mAP: A Unified Protocol for Occlusion, Joint-Angle, and Temporal Evaluation of 2D Pose Estimators
2026-10-22 11:17:52Yifan FanAbstract
The dominant metric for 2D human pose estimation, OKS-mAP, has three blind spots: it does not distinguish visible from occluded keypoints, does not measure joint-angle error, and is silent on the temporal stability of tracking-free per-frame inference. We propose a supplementary protocol with three modules: (M1) OKS-mAP stratified by ground-truth visibility, with Delta_occ = mAP(v=1) - mAP(full), quantifying how much the aggregate metric masks occlusion-specific differences; (M2) joint-angle error (RMSE/MAE/P95) plus knee-valgus detection consistency; (M3) frame-to-frame displacement, median-filter residual jitter, and jump rate, before and after OneEuro smoothing. We ran it on four datasets (COCO val2017, CrowdPose, OCHuman, PoseTrack18) and four models from two architecture families (YOLO11s-pose, an occlusion-domain fine-tuned variant, ViTPose-B/L), under a common detector-free oracle protocol and an open-source harness. Delta_occ is negative in every combination (-0.19 to -0.47): aggregate mAP overestimates occluded-scene performance in every tested combination. Joint-angle error is amplified in occluded layers; angle metrics reveal differences mAP cannot localize, e.g., the smaller occlusion-trained model significantly outperforming a larger transformer in occluded elbows. On PoseTrack18 val, raw jitter matched between the two YOLO models; pixel-domain OneEuro smoothing cut jump rates 9-10% at no matched-scope AP cost; residual jitter persists after detector removal; the transformer pair's jump rate is significantly lower; smoothing benefits replicate across architectures. Stratified mAP tracks full mAP closely (sign-consistency 1.0, 12/12), serving magnitude diagnosis rather than re-ranking; angle-metric difference vectors (especially knee) partially diverge, suggesting independent information. The protocol and harness are open-sourced, complementing rather than replacing OKS-mAP.
Abstract (translated)
URL
https://arxiv.org/abs/2611.12911
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UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception
2026-10-21 14:02:25Linfei Li, Ruining Hu, Lin Zhang, Zhong Wang, Fengyi Zhang, Ying Shen, Binqiang Wang, Xin ZhangAbstract
Vision-language perception has achieved impressive progress in aligning natural language with visual observations, yet grounding high-level semantics into part-level physical interaction remains challenging. To address this gap, we propose UniRAP, a unified model for inferring part-level physical affordances and mapping language instructions to actionable geometric representations. UniRAP formulates this problem as a conditional multimodal generation task, integrating visual inputs, textual instructions, and optional prompts into a shared spatiotemporal representation space through a unified interface token mechanism. To predict executable contact geometry, we introduce a Unified Affordance Decoder (UAD), which jointly performs object detection, part-level affordance segmentation, and 4-DoF interaction pose estimation by leveraging intermediate segmentation features. In addition, we propose a curriculum-based transfer training strategy that progressively adapts the model from general visual parsing to interaction-aware perception, improving data efficiency under limited textual annotations. Experiments show that UniRAP achieves state-of-the-art performance on referring expression segmentation, affordance grounding, and interaction pose estimation, while maintaining strong spatiotemporal consistency in dynamic video scenarios. These results demonstrate the effectiveness of UniRAP as a unified perception framework for language-guided physical manipulation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29768
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BlazeVGGT: 13x Faster VGGT at 2,000 Frames via Camera Token without Training
2026-10-21 09:11:19Shujun Yao, Junhui Lin, Congying Han, Tiande Guo, Ziwen Liu, Bonan LiAbstract
Feed-forward visual geometry models, exemplified by VGGT, have demonstrated strong multi-view reconstruction capabilities through dense global attention. However, the resulting all-to-all cross-view computation scales quadratically with the number of input views, making long-sequence inference particularly expensive due to substantial frame-level redundancy among highly overlapping observations. We uncover a topology-first, geometry-later evolution in VGGT camera tokens: shallow-layer tokens reliably capture local view neighborhoods, whereas deeper-layer tokens progressively encode globally organized inter-view geometry. Motivated by this observation, we introduce BlazeVGGT, a training-free framework for structure-aware frame-level key--value sparsification. At each global-attention block, BlazeVGGT constructs a view-level similarity kernel from camera-token relations and employs greedy determinantal point process inference to select a compact set of mutually complementary views. The selected views constitute the key--value context, while all input frames are retained as queries. This asymmetric attention design preserves dense per-frame predictions while eliminating redundant cross-view computation, allowing our method to be directly integrated into pretrained VGGT as a plug-and-play module without parameter updates. Its advantages become increasingly pronounced with sequence length: at 2,000 frames, BlazeVGGT achieves approximately 13$\times$ faster inference than VGGT while reducing the Chamfer Distance from 0.41 to 0.32. Extensive experiments demonstrate a favorable accuracy--efficiency trade-off in point-cloud reconstruction and camera-pose estimation, compatibility with existing VGGT accelerators such as FastVGGT and LiteVGGT, and transferability to VGGT-based models including StreamVGGT and PAGE-4D. Codes are publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29454
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VLM-Guided Contact Retargeting and Generative Partner Modeling for Humanoid-Human Interaction
2026-10-20 16:57:52Yuto Shibata, Kotaro Amaya, Kashu Yamazaki, Lalit Jayanti, Yoshimitsu Aoki, Mariko Isogawa, Katerina FragkiadakiAbstract
Humanoid-human interaction requires robots to operate under severe partial observability: during close physical contact, the human partner is occluded, truncated, or outside the robot's field of view, causing pose estimation to fail and often necessitating external tracking or privileged human state. At the same time, heavy mutual occlusions introduce errors into the human-human motion-capture data used for learning. We present REACT, a framework for learning deployable humanoid-human interaction policies from human-human demonstrations. REACT treats semantic contact structure as the key supervisory signal connecting demonstration processing, interaction learning, and onboard control. First, a VLM-guided contact modeling and retargeting pipeline identifies semantically meaningful contacts and transfers them onto humanoid-human geometry while preserving interaction structure. Using these annotations, privileged interaction policies learn physically grounded behaviors through interaction-aware contact rewards and are then distilled into deployable egocentric depth policies trained entirely from onboard sensing using photorealistic human depth rendering. To address severe partial observability, we introduce generative partner-state modeling objectives that predict partner motion from occluded egocentric views. We evaluate REACT in simulation and on a real Unitree G1 humanoid across five contact-rich interactions. Semantic contact modeling substantially improves preservation of demonstrated interactions, while generative partner-state modeling improves contact recall on four of five interactions, most under heavy occlusion. On real hardware, REACT performs all five interactions using onboard sensing alone.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28523
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CrossFit-GS: Out-of-Fold Pose Estimation for COLMAP-Free 3D Gaussian Splatting
2026-10-20 13:29:26Zijian Wu, Jinliang Wang, Mingfeng Jiang, Zidian Lin, Ying Song, Ziqian LuAbstract
Pose-free 3D Gaussian Splatting (3DGS) aims to bypass cumbersome Structure-from-Motion (SfM) by directly reconstructing scenes from unposed image sequences. In this work, we present CrossFit-GS, a deferred-commitment progressive framework that mitigates cumulative trajectory drift and breaks the self-reinforcing loop of distorted-map error propagation. Specifically, we partition the input sequence into interleaved subsets and maintain two complementary submaps. Each candidate pose is cross-refined against a frozen opposite submap that has never been optimized using the target frame, thereby providing an independent geometric reference for pose estimation. Checkerboard validation and spatial coverage safeguards further reject unreliable updates, and only verified poses are committed to their corresponding owner submap. Once the trajectory has been refined, the intermediate submaps are discarded and the final global representation is trained from scratch using the fixed refined poses. Extensive evaluations across multiple benchmarks demonstrate that CrossFit-GS substantially reduces cumulative trajectory drift and consistently improves both camera pose estimation and novel-view synthesis over existing methods.
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URL
https://arxiv.org/abs/2610.28190
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Real-Time Ground Normal Vector Estimation for Spherical Mobile 3D Mapping using LiDAR-Inertial Odometry
2026-10-20 11:30:56Tim Schubert, Fabian Arzberger, Andreas N\"uchterAbstract
Accurate ground plane normal estimation is essential for odometry in spherical mobile mapping systems (SMMS), which face challenges from rapid rotations and limited ground coverage. We propose two RANSAC-based approaches: single-scan estimation with normal vector scoring and multi-step validation, and LiDAR-inertial odometry (LIO)-based estimation using a global 3D map. Evaluated across diverse real-world datasets against terrestrial laser scanner ground truth, results show that LIO-based estimation significantly outperforms single-scan methods in accuracy and robustness. Integrating ground normal estimates into the motion model improves pose estimation, supporting autonomous navigation in challenging and hazardous environments.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28701
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PnP-Transformer: A Neural Pose Solver for Zero-Shot 6D Object Pose Estimation
2026-10-20 08:21:44Emirkan Burak Yilmaz, Yakup GencAbstract
We present PnP-Transformer, a neural pose solver designed as an iterative-free, differentiable alternative to classical PnP-RANSAC algorithms. PnP-Transformer regresses the 6D object pose by processing predicted semi-dense 2D-3D correspondence fields alongside confidence scores, while mapping geometric inputs into a continuous latent space. To evaluate its practical utility, we integrate PnP-Transformer into a zero-shot 6D pose estimation pipeline, where the correspondence module and pose solver are trained separately. PnP-Transformer achieves a 41.2% mean AR score with low latency (0.46 s/obj) on seven core BOP benchmark datasets, surpassing all prior baselines (such as RayPose with 39.1% AR) but one (Co-op with 58.4% AR). When the pose solver is replaced with EPnP-RANSAC (similar to Co-op) while keeping the correspondence module unchanged, the RANSAC alternative outperforms PnP-Transformer on six of the seven datasets by reaching 52.2% mean AR at a cost of an additional 60 ms per object, revealing an accuracy-latency trade-off. Code and pretrained models are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26096
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