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Only Research#Open source
  1. 3D视觉工坊 · 微信公众号78

    Any3R goes open source: one model unifies 3D reconstruction and pose estimation for pinhole, fisheye, and panoramic cameras

    South Dakota State University and other institutions propose Any3R, which uses a dense ray field to describe arbitrary central cameras, reconstructing a 3D point cloud and estimating pose from mixed-camera images in a single forward pass. The method replaces the row and column indices of rotary position encoding with angular coordinates and weights the pixel loss by solid angle, so one set of weights covers pinhole, fisheye beyond 180°, and 360° panoramic cameras. On benchmarks spanning 13 central camera types and 3 datasets, it leads in both reconstruction accuracy and pose accuracy; the inference code, 13 camera models, and weights are open-sourced.

    Why it matters: The article presents a feed-forward 3D reconstruction method that replaces the pinhole assumption with a dense ray field, offering insight into handling mixed inputs from multiple camera types.

  2. arXiv cs.RO78

    ∇Mimic: Efficient Whole-Body Control for Humanoid Robots with Differentiable Simulation

    The authors propose ∇Mimic, which uses exact gradients from a differentiable simulator to train whole-body motion-tracking policies for humanoid robots, reaching a score of 0.82 in 20 minutes and 0.874 in 1.8 hours on a single GPU, whereas the world-model method takes 1.2 hours to reach 0.82.

    Why it matters: The paper presents a complete scheme for training humanoid policies with exact gradients under differentiable simulation and open-sources the engine and code, making it easy to compare the efficiency of first-order and zeroth-order methods.

  1. 3D视觉工坊 · 微信公众号78

    HKU open-sources GenSfM: jointly reconstructing 3D objects and camera poses from four uncalibrated images

    A University of Hong Kong team has open-sourced GenSfM, which uses a native 3D diffusion model to jointly denoise object shape and per-view camera poses within a single multimodal sequence, requiring only 2 to 4 uncalibrated images and no camera intrinsics or extrinsics. Evaluated on Toys4k and GSO, it improves novel-view PSNR by 3.2–4.5 dB over the multi-view baseline TRELLIS-M, reduces median Chamfer distance by about 64%–74%, and achieves pose Acc@5° above 96%.

    Why it matters: The work turns a native 3D diffusion model into the main inference mechanism for joint denoising of shape and camera poses, and releases code and models as a reference for sparse-view reconstruction.

  1. arXiv cs.RO76

    RAX: An open-source framework for scaling robot agent capabilities through online exploration

    The paper proposes RAX (Robot Agent eXploration), an open-source robot agent framework centered on scaling online exploration, in which a general-purpose coding agent generates robot control code without using action demonstration data, hand-engineered action primitives, or value-function guidance.

    Why it matters: The paper compares the imitation learning paradigm that relies on demonstration data with a demonstration-free online exploration approach, and reports quantitative gaps in simulation and on real robots, helping readers assess the feasibility of this direction.

  1. 简智机器人 · X71

    Genrobot open-sources Phase 3 UMI dual-arm manipulation dataset

    Genrobot has open-sourced the Phase 3 UMI dataset, containing 13K+ hours of dual-arm data that is ready to use and supports sub-1ms frame-level synchronization and high-fidelity trajectory reconstruction. The data was collected by 3K+ contributors in 10K+ real homes, covering diverse everyday manipulation skills. Download at https://huggingface.co/datasets/genrobot2025/10Kh-RealOmin-OpenData.

    Why it matters: The data scale and real-home collection background help readers judge whether this kind of UMI data can be used to train their own manipulation tasks.

  1. 松灵机器人66

    NYU open-sources YOR, a low-cost dual-arm mobile robot, with bill of materials under $10,000

    A research team at New York University has open-sourced YOR (Your Own Robot), a mobile dual-arm manipulation robot, with a bill of materials cost below $10,000. The project homepage is www.yourownrobot.ai, and the paper is at arxiv.org/abs/2602.11150.

    Why it matters: An open-source dual-arm mobile platform under $10,000 offers a reproducible hardware path and measured data, making it suitable for evaluating the cost and capability trade-offs of building a mobile manipulation system in-house.

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