Qian Li

Rao Fu, Cheng Wen, Qian Li et al.

This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from Bézier decomposition on NURBS models, we transfer it to guide point cloud segmentation casting off primitive types. A joint optimization framework is proposed to learn Bézier primitive segmentation and geometric fitting simultaneously on a cascaded architecture. Specifically, we introduce a soft voting regularizer to improve primitive segmentation and propose an auto-weight embedding module to cluster point features, making the network more robust and generic. We also introduce a reconstruction module where we successfully process multiple CAD models with different primitives simultaneously. We conducted extensive experiments on the synthetic ABC dataset and real-scan datasets to validate and compare our approach with different baseline methods. Experiments show superior performance over previous work in terms of segmentation, with a substantially faster inference speed.

Dayong Wu, Jiaqi Li, Baoxin Wang et al.

Large language models (LLMs) have shown remarkable achievements across various language tasks.To enhance the performance of LLMs in scientific literature services, we developed the scientific literature LLM (SciLit-LLM) through pre-training and supervised fine-tuning on scientific literature, building upon the iFLYTEK Spark LLM. Furthermore, we present a knowledge service system Spark Research Assistant (SparkRA) based on our SciLit-LLM. SparkRA is accessible online and provides three primary functions: literature investigation, paper reading, and academic writing. As of July 30, 2024, SparkRA has garnered over 50,000 registered users, with a total usage count exceeding 1.3 million.

Qian Li, Shuojue Yang, Daiyun Shen et al.

High-fidelity reconstruction of surgical scene is a fundamentally crucial task to support many applications, such as intra-operative navigation and surgical education. However, most existing methods assume the ideal surgical scenarios - either focus on dynamic reconstruction with deforming tissue yet assuming a given fixed camera pose, or allow endoscope movement yet reconstructing the static scenes. In this paper, we target at a more realistic yet challenging setup - free-pose reconstruction with a moving camera for highly dynamic surgical scenes. Meanwhile, we take the first step to introduce Gaussian Splitting (GS) technique to tackle this challenging setting and propose a novel GS-based framework for fast reconstruction, termed \textit{Free-DyGS}. Concretely, our model embraces a novel scene initialization in which a pre-trained Sparse Gaussian Regressor (SGR) can efficiently parameterize the initial attributes. For each subsequent frame, we propose to jointly optimize the deformation model and 6D camera poses in a frame-by-frame manner, easing training given the limited deformation differences between consecutive frames. A Scene Expansion scheme is followed to expand the GS model for the unseen regions introduced by the moving camera. Moreover, the framework is equipped with a novel Retrospective Deformation Recapitulation (RDR) strategy to preserve the entire-clip deformations throughout the frame-by-frame training scheme. The efficacy of the proposed Free-DyGS is substantiated through extensive experiments on two datasets: StereoMIS and Hamlyn datasets. The experimental outcomes underscore that Free-DyGS surpasses other advanced methods in both rendering accuracy and efficiency. Code will be available.

Shuojue Yang, Zijian Wu, Mingxuan Hong et al.

Real2Sim is becoming increasingly important with the rapid development of surgical artificial intelligence (AI) and autonomy. In this work, we propose a novel Real2Sim methodology, Instrument-Splatting, that leverages 3D Gaussian Splatting to provide fully controllable 3D reconstruction of surgical instruments from monocular surgical videos. To maintain both high visual fidelity and manipulability, we introduce a geometry pre-training to bind Gaussian point clouds on part mesh with accurate geometric priors and define a forward kinematics to control the Gaussians as flexible as real instruments. Afterward, to handle unposed videos, we design a novel instrument pose tracking method leveraging semantics-embedded Gaussians to robustly refine per-frame instrument poses and joint states in a render-and-compare manner, which allows our instrument Gaussian to accurately learn textures and reach photorealistic rendering. We validated our method on 2 publicly released surgical videos and 4 videos collected on ex vivo tissues and green screens. Quantitative and qualitative evaluations demonstrate the effectiveness and superiority of the proposed method.