arXiv subjects

Jingpei Lu

Publications and source records attributed to Jingpei Lu.

At least 19 recordsLinked to original sources

arXiv2026

Seeing Through Smoke: Surgical Desmoking for Improved Visual Perception

Minimally invasive and robot-assisted surgery relies heavily on endoscopic imaging, yet surgical smoke produced by electrocautery and vessel-sealing instruments can severely degrade visual perception and hinder vision-based functionalities. We present a transformer-based surgical desmoking model with a physics-inspired desmoking head that jointly predicts smoke-free image and corresponding smoke map. To address the scarcity of paired smoky-to-smoke-free training data, we develop a synthetic data generation pipeline that blends artificial smoke patterns with real endoscopic images, yielding over 80,000 paired samples for supervised training. We further curate, to our knowledge, the largest paired surgical smoke dataset to date, comprising 5,817 image pairs captured with the da Vinci robotic surgical system, enabling benchmarking on high-resolution endoscopic images. Extensive experiments on both a public benchmark and our dataset demonstrate state-of-the-art performance in image reconstruction compared to existing dehazing and desmoking approaches. We also assess the impact of desmoking on downstream stereo depth estimation and instrument segmentation, highlighting both the potential benefits and current limitations of digital smoke removal methods.

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arXiv2025

SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures

High-resolution imaging is crucial for enhancing visual clarity and enabling precise computer-assisted guidance in minimally invasive surgery (MIS). Despite the increasing adoption of 4K endoscopic systems, there remains a significant gap in publicly available native 4K datasets tailored specifically for robotic-assisted MIS. We introduce SurgiSR4K, the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution, representing realistic conditions of robotic-assisted procedures. SurgiSR4K comprises diverse visual scenarios including specular reflections, tool occlusions, bleeding, and soft tissue deformations, meticulously designed to reflect common challenges faced during laparoscopic and robotic surgeries. This dataset opens up possibilities for a broad range of computer vision tasks that might benefit from high resolution data, such as super resolution (SR), smoke removal, surgical instrument detection, 3D tissue reconstruction, monocular depth estimation, instance segmentation, novel view synthesis, and vision-language model (VLM) development. SurgiSR4K provides a robust foundation for advancing research in high-resolution surgical imaging and fosters the development of intelligent imaging technologies aimed at enhancing performance, safety, and usability in image-guided robotic surgeries.

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arXiv2024

CtRNet-X: Camera-to-Robot Pose Estimation in Real-world Conditions Using a Single Camera

Camera-to-robot calibration is crucial for vision-based robot control and requires effort to make it accurate. Recent advancements in markerless pose estimation methods have eliminated the need for time-consuming physical setups for camera-to-robot calibration. While the existing markerless pose estimation methods have demonstrated impressive accuracy without the need for cumbersome setups, they rely on the assumption that all the robot joints are visible within the camera's field of view. However, in practice, robots usually move in and out of view, and some portion of the robot may stay out-of-frame during the whole manipulation task due to real-world constraints, leading to a lack of sufficient visual features and subsequent failure of these approaches. To address this challenge and enhance the applicability to vision-based robot control, we propose a novel framework capable of estimating the robot pose with partially visible robot manipulators. Our approach leverages the Vision-Language Models for fine-grained robot components detection, and integrates it into a keypoint-based pose estimation network, which enables more robust performance in varied operational conditions. The framework is evaluated on both public robot datasets and self-collected partial-view datasets to demonstrate our robustness and generalizability. As a result, this method is effective for robot pose estimation in a wider range of real-world manipulation scenarios.

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arXiv2024

HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management

Hemorrhaging occurs in surgeries of all types, forcing surgeons to quickly adapt to the visual interference that results from blood rapidly filling the surgical field. Introducing automation into the crucial surgical task of hemostasis management would offload mental and physical tasks from the surgeon and surgical assistants while simultaneously increasing the efficiency and safety of the operation. The first step in automation of hemostasis management is detection of blood in the surgical field. To propel the development of blood detection algorithms in surgeries, we present HemoSet, the first blood segmentation dataset based on bleeding during a live animal robotic surgery. Our dataset features vessel hemorrhage scenarios where turbulent flow leads to abnormal pooling geometries in surgical fields. These pools are formed in conditions endemic to surgical procedures -- uneven heterogeneous tissue, under glossy lighting conditions and rapid tool movement. We benchmark several state-of-the-art segmentation models and provide insight into the difficulties specific to blood detection. We intend for HemoSet to spur development of autonomous blood suction tools by providing a platform for training and refining blood segmentation models, addressing the precision needed for such robotics.

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arXiv2024

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.

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arXiv2023

BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields

Reconstruction of deformable scenes from endoscopic videos is important for many applications such as intraoperative navigation, surgical visual perception, and robotic surgery. It is a foundational requirement for realizing autonomous robotic interventions for minimally invasive surgery. However, previous approaches in this domain have been limited by their modular nature and are confined to specific camera and scene settings. Our work adopts the Neural Radiance Fields (NeRF) approach to learning 3D implicit representations of scenes that are both dynamic and deformable over time, and furthermore with unknown camera poses. We demonstrate this approach on endoscopic surgical scenes from robotic surgery. This work removes the constraints of known camera poses and overcomes the drawbacks of the state-of-the-art unstructured dynamic scene reconstruction technique, which relies on the static part of the scene for accurate reconstruction. Through several experimental datasets, we demonstrate the versatility of our proposed model to adapt to diverse camera and scene settings, and show its promise for both current and future robotic surgical systems.

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arXiv2023

Tracking Snake-like Robots in the Wild Using Only a Single Camera

Robot navigation within complex environments requires precise state estimation and localization to ensure robust and safe operations. For ambulating mobile robots like robot snakes, traditional methods for sensing require multiple embedded sensors or markers, leading to increased complexity, cost, and increased points of failure. Alternatively, deploying an external camera in the environment is very easy to do, and marker-less state estimation of the robot from this camera's images is an ideal solution: both simple and cost-effective. However, the challenge in this process is in tracking the robot under larger environments where the cameras may be moved around without extrinsic calibration, or maybe when in motion (e.g., a drone following the robot). The scenario itself presents a complex challenge: single-image reconstruction of robot poses under noisy observations. In this paper, we address the problem of tracking ambulatory mobile robots from a single camera. The method combines differentiable rendering with the Kalman filter. This synergy allows for simultaneous estimation of the robot's joint angle and pose while also providing state uncertainty which could be used later on for robust control. We demonstrate the efficacy of our approach on a snake-like robot in both stationary and non-stationary (moving) cameras, validating its performance in both structured and unstructured scenarios. The results achieved show an average error of 0.05 m in localizing the robot's base position and 6 degrees in joint state estimation. We believe this novel technique opens up possibilities for enhanced robot mobility and navigation in future exploratory and search-and-rescue missions.

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arXiv2023

SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data

A major source of endoscopic tissue tracking errors during deformations stems from wrong data association between observed sensor measurements with previously tracked scene. To mitigate this issue, we present a surgical perception framework, SuPerPM, that leverages learning-based non-rigid point cloud matching for data association, thus accommodating larger deformations than previous approaches which relied on Iterative Closest Point (ICP) for point associations. The learning models typically require training data with ground truth point cloud correspondences, which is challenging or even impractical to collect in surgical environments. Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth. This was achieved by employing a position-based dynamics (PBD) simulation to ensure that the correspondences adhered to physical constraints. The proposed framework is demonstrated on several challenging surgical datasets that are characterized by large deformations, achieving superior performance over advanced surgical scene tracking algorithms.

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arXiv2023

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives

Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. To successfully navigate and interact in dynamic settings, robots require the understanding of their spatial surroundings driven by unassisted reconstruction of 3D scenes and camera poses from real-time video footage. Existing approaches like COLMAP and bundle-adjusting neural radiance field methods take hours to days to process due to the high computational demands of feature matching, dense point sampling, and training of a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP) which leverages accelerated sampling and hash encoding to expedite automatic pose refinement/estimation and 3D scene reconstruction. Experimental results demonstrate 10 to 20 x speed improvement compared to other bundle-adjusting neural radiance field methods without sacrificing the quality of pose estimation. The github repository can be found here https://github.com/IntelLabs/baa-ngp.

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arXiv2023

Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer

Solving the camera-to-robot pose is a fundamental requirement for vision-based robot control, and is a process that takes considerable effort and cares to make accurate. Traditional approaches require modification of the robot via markers, and subsequent deep learning approaches enabled markerless feature extraction. Mainstream deep learning methods only use synthetic data and rely on Domain Randomization to fill the sim-to-real gap, because acquiring the 3D annotation is labor-intensive. In this work, we go beyond the limitation of 3D annotations for real-world data. We propose an end-to-end pose estimation framework that is capable of online camera-to-robot calibration and a self-supervised training method to scale the training to unlabeled real-world data. Our framework combines deep learning and geometric vision for solving the robot pose, and the pipeline is fully differentiable. To train the Camera-to-Robot Pose Estimation Network (CtRNet), we leverage foreground segmentation and differentiable rendering for image-level self-supervision. The pose prediction is visualized through a renderer and the image loss with the input image is back-propagated to train the neural network. Our experimental results on two public real datasets confirm the effectiveness of our approach over existing works. We also integrate our framework into a visual servoing system to demonstrate the promise of real-time precise robot pose estimation for automation tasks.

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arXiv2023

Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Rendering

State estimation from measured data is crucial for robotic applications as autonomous systems rely on sensors to capture the motion and localize in the 3D world. Among sensors that are designed for measuring a robot's pose, or for soft robots, their shape, vision sensors are favorable because they are information-rich, easy to set up, and cost-effective. With recent advancements in computer vision, deep learning-based methods no longer require markers for identifying feature points on the robot. However, learning-based methods are data-hungry and hence not suitable for soft and prototyping robots, as building such bench-marking datasets is usually infeasible. In this work, we achieve image-based robot pose estimation and shape reconstruction from camera images. Our method requires no precise robot meshes, but rather utilizes a differentiable renderer and primitive shapes. It hence can be applied to robots for which CAD models might not be available or are crude. Our parameter estimation pipeline is fully differentiable. The robot shape and pose are estimated iteratively by back-propagating the image loss to update the parameters. We demonstrate that our method of using geometrical shape primitives can achieve high accuracy in shape reconstruction for a soft continuum robot and pose estimation for a robot manipulator.

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arXiv2022

Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking

Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed framework is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of the-art approaches. Our code and dataset are available at https://github.com/ucsdarclab/Python-SuPer.

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arXiv2022

Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics

Robot manipulation of rope-like objects is an interesting problem that has some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a model-based method that could be used to solve a large variety of optimization problems. Towards this end, we introduced compliant, position-based dynamics (XPBD) to model rope-like objects. Using geometric constraints, the model can represent the coupling of shear/stretch and bend/twist effects. Of crucial importance is that our formulation is differentiable, which can solve parameter estimation problems and improve the matching of rope physics to real-life scenarios (i.e., the real-to-sim problem). For the generality of rope-like objects, two different solvers are proposed to handle the inextensible and extensible effects of varied material stiffness for the rope. We demonstrate our framework's robustness and accuracy on real-to-sim experimental setups using the Baxter robot and the da Vinci research kit (DVRK). Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.

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arXiv2022

Parameter Identification and Motion Control for Articulated Rigid Body Robots Using Differentiable Position-based Dynamics

Simulation modeling of robots, objects, and environments is the backbone for all model-based control and learning. It is leveraged broadly across dynamic programming and model-predictive control, as well as data generation for imitation, transfer, and reinforcement learning. In addition to fidelity, key features of models in these control and learning contexts are speed, stability, and native differentiability. However, many popular simulation platforms for robotics today lack at least one of the features above. More recently, position-based dynamics (PBD) has become a very popular simulation tool for modeling complex scenes of rigid and non-rigid object interactions, due to its speed and stability, and is starting to gain significant interest in robotics for its potential use in model-based control and learning. Thus, in this paper, we present a mathematical formulation for coupling position-based dynamics (PBD) simulation and optimal robot design, model-based motion control and system identification. Our framework breaks down PBD definitions and derivations for various types of joint-based articulated rigid bodies. We present a back-propagation method with automatic differentiation, which can integrate both positional and angular geometric constraints. Our framework can critically provide the native gradient information and perform gradient-based optimization tasks. We also propose articulated joint model representations and simulation workflow for our differentiable framework. We demonstrate the capability of the framework in efficient optimal robot design, accurate trajectory torque estimation and supporting spring stiffness estimation, where we achieve minor errors. We also implement impedance control in real robots to demonstrate the potential of our differentiable framework in human-in-the-loop applications.

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arXiv2021

Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approach

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are non-identifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call Lumped Error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the Lumped Error and show the efficiency of this parameter reduction.

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arXiv2021

Real-to-Sim Registration of Deformable Soft Tissue with Position-Based Dynamics for Surgical Robot Autonomy

Autonomy in robotic surgery is very challenging in unstructured environments, especially when interacting with deformable soft tissues. The main difficulty is to generate model-based control methods that account for deformation dynamics during tissue manipulation. Previous works in vision-based perception can capture the geometric changes within the scene, however, model-based controllers integrated with dynamic properties, a more accurate and safe approach, has not been studied before. Considering the mechanic coupling between the robot and the environment, it is crucial to develop a registered, simulated dynamical model. In this work, we propose an online, continuous, real-to-sim registration method to bridge 3D visual perception with position-based dynamics (PBD) modeling of tissues. The PBD method is employed to simulate soft tissue dynamics as well as rigid tool interactions for model-based control. Meanwhile, a vision-based strategy is used to generate 3D reconstructed point cloud surfaces based on real-world manipulation, so as to register and update the simulation. To verify this real-to-sim approach, tissue experiments have been conducted on the da Vinci Research Kit. Our real-to-sim approach successfully reduces registration error online, which is especially important for safety during autonomous control. Moreover, it achieves higher accuracy in occluded areas than fusion-based reconstruction.

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arXiv2021

SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction

Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue. Previous works on surgical perception frameworks require significant effort in developing features for surgical tool and tissue tracking. In this work, we overcome the challenge by exploiting deep learning methods for surgical perception. We integrated deep neural networks, capable of efficient feature extraction, into the tissue reconstruction and instrument pose estimation processes. By leveraging transfer learning, the deep learning based approach requires minimal training data and reduced feature engineering efforts to fully perceive a surgical scene. The framework was tested on three publicly available datasets, which use the da Vinci Surgical System, for comprehensive analysis. Experimental results show that our framework achieves state-of-the-art tracking performance in a surgical environment by utilizing deep learning for feature extraction.

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arXiv2020

Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer

Keypoint detection is an essential building block for many robotic applications like motion capture and pose estimation. Historically, keypoints are detected using uniquely engineered markers such as checkerboards or fiducials. More recently, deep learning methods have been explored as they have the ability to detect user-defined keypoints in a marker-less manner. However, different manually selected keypoints can have uneven performance when it comes to detection and localization. An example of this can be found on symmetric robotic tools where DNN detectors cannot solve the correspondence problem correctly. In this work, we propose a new and autonomous way to define the keypoint locations that overcomes these challenges. The approach involves finding the optimal set of keypoints on robotic manipulators for robust visual detection and localization. Using a robotic simulator as a medium, our algorithm utilizes synthetic data for DNN training, and the proposed algorithm is used to optimize the selection of keypoints through an iterative approach. The results show that when using the optimized keypoints, the detection performance of the DNNs improved significantly. We further use the optimized keypoints for real robotic applications by using domain randomization to bridge the reality gap between the simulator and the physical world. The physical world experiments show how the proposed method can be applied to the wide-breadth of robotic applications that require visual feedback, such as camera-to-robot calibration, robotic tool tracking, and end-effector pose estimation.

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