We introduce Robowheel, a data engine that converts human hand-object interaction (HOI) videos into training-ready supervision for cross-morphology robotic learning. From monocular RGB/RGB-D inputs, we perform high-precision HOI reconstruction and enforce physical plausibility via a reinforcement learning (RL) optimizer that refines hand-object relative poses under contact and penetration constraints. The reconstructed, contact-rich trajectories are then retargeted to cross-embodiments, robot arms with simple end-effectors, dexterous hands, and humanoids, yielding executable actions and rollouts. To scale coverage, we build a simulation-augmented framework on Isaac Sim with diverse domain randomization (embodiments, trajectories, object retrieval, background textures, hand motion mirroring), which enriches the distributions of trajectories and observations while preserving spatial relationships and physical plausibility. The entire data pipeline forms an end-to-end pipeline from video - reconstruction - retargeting - augmentation - data acquisition.We validate the data on mainstream vision-language-action (VLA) and imitation learning architectures, demonstrating that trajectories produced by our pipeline are as stable as those from teleoperation and yield comparable continual performance gains. To our knowledge, this provides the first quantitative evidence that HOI modalities can serve as effective supervision for robotic learning. Compared with teleoperation, Robowheel is lightweight: a single monocular RGB(D) camera is sufficient to extract a universal, embodiment-agnostic motion representation that could be flexibly retargeted across embodiments. We further assemble a large-scale multimodal dataset combining multi-camera captures, monocular videos, and public HOI corpora for training and evaluating embodied models.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Modern mapping tools remain fundamentally point-and-click systems, offering limited support for interactive, multimodal engagement with either map views or real-world camera inputs. This limitation becomes most apparent in the last 100 meters of navigation and during in-situ local discovery, where users naturally combine spatial reasoning over maps with egocentric perception of their surroundings. We introduce IMAIA, an interactive geospatial assistance framework that unifies three core capabilities: (1) map-centric spatial understanding, (2) camera-to-place grounding, and (3) human-centered, bearing-aware navigation. IMAIA consists of two interoperable components--Maps Plus and PAISA--coordinated by a lightweight multi-agent orchestrator. Maps Plus converts vector and satellite maps into a grid-aligned representation that enables flexible, view-conditioned spatial queries. PAISA fuses camera imagery with geospatial signals such as location, heading, and proximity to interpret egocentric scenes and surface relevant attributes of nearby places. The framework is fully modular: vision-language backends can be replaced without altering system behavior. IMAIA enables responsive, interpretable, and context-aware assistance across both map-based and real-world interactions, supporting a new class of interactive mapping experiences that are practical for real-world deployment.
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cs.CV · cs.MA
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Panoramic depth estimation enables a complete 360^\circ understanding of 3D environments but faces significant challenges in generalizing to real-world scenes. While recent zero-shot depth models like Depth Anything achieve remarkable generalization on perspective images, their performance sharply degrades on panoramas due to projection distortions and the lack of spherical geometric awareness. Moreover, collecting large-scale panoramic RGB-D data is costly, hindering the large-scale training of panoramic foundation models. To address these issues, we propose an SO(3)-Equivariant ViT-Adapter, which transfers the powerful zero-shot capability of the perspective pre-trained ViT to panoramic depth estimation by explicitly incorporating a rotation-equivariant inductive bias. Our adapter introduces an SO(3) deformable cross-attention mechanism to effectively align SO(3)-equivariant features with perspective features, enhancing rotational consistency without modifying the ViT backbone. Trained solely on synthetic panoramas, our framework achieves robust zero-shot sim-to-real performance on real indoor benchmarks, including Matterport3D and Stanford2D3D, demonstrating both data efficiency and strong generalization for panoramic depth estimation.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表