Paper Reading AI Learner

Future Frame Prediction for Robot-assisted Surgery

2021-03-18 15:12:06
Xiaojie Gao, Yueming Jin, Zixu Zhao, Qi Dou, Pheng-Ann Heng

Abstract

Predicting future frames for robotic surgical video is an interesting, important yet extremely challenging problem, given that the operative tasks may have complex dynamics. Existing approaches on future prediction of natural videos were based on either deterministic models or stochastic models, including deep recurrent neural networks, optical flow, and latent space modeling. However, the potential in predicting meaningful movements of robots with dual arms in surgical scenarios has not been tapped so far, which is typically more challenging than forecasting independent motions of one arm robots in natural scenarios. In this paper, we propose a ternary prior guided variational autoencoder (TPG-VAE) model for future frame prediction in robotic surgical video sequences. Besides content distribution, our model learns motion distribution, which is novel to handle the small movements of surgical tools. Furthermore, we add the invariant prior information from the gesture class into the generation process to constrain the latent space of our model. To our best knowledge, this is the first time that the future frames of dual arm robots are predicted considering their unique characteristics relative to general robotic videos. Experiments demonstrate that our model gains more stable and realistic future frame prediction scenes with the suturing task on the public JIGSAWS dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2103.10308

PDF

https://arxiv.org/pdf/2103.10308.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot