Search Results for author:
Found 38 papers, 16 papers with code
Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World
In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown.
Image-Goal Navigation Using Refined Feature Guidance and Scene Graph Enhancement
In this paper, we introduce a novel image-goal navigation approach, named RFSG.
Overcoming Support Dilution for Robust Few-shot Semantic Segmentation
Few-shot Semantic Segmentation (FSS) is a challenging task that utilizes limited support images to segment associated unseen objects in query images.
Adapting Segment Anything Model for Unseen Object Instance Segmentation
Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments.
Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments
Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world.
UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies.
SR-Mamba: Effective Surgical Phase Recognition with State Space Model
Inspired by the recent success of Mamba, a state space model with linear scalability in sequence length, this paper presents SR-Mamba, a novel attention-free model specifically tailored to meet the challenges of surgical phase recognition.
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
To tackle this problem, in this paper, we propose the first SfM-free 3DGS-based method for surgical scene reconstruction by jointly optimizing the camera poses and scene representation.
Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search
Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness.
Scalable 3D Registration via Truncated Entry-wise Absolute Residuals
Given an input set of $3$D point pairs, the goal of outlier-robust $3$D registration is to compute some rotation and translation that align as many point pairs as possible.
Neural Markov Random Field for Stereo Matching
Stereo matching is a core task for many computer vision and robotics applications.
Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-Template Matching
Soft tissue tracking is crucial for computer-assisted interventions.
SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store
In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain.
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
This work aims to improve unsupervised audio-visual pre-training.
Efficient Map Sparsification Based on 2D and 3D Discretized Grids
Furthermore, to reduce the influence of different spatial distributions between the mapping and query sequences, which is not considered in previous methods, we also introduce a space constraint term based on 3D discretized grids.
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot
Task automation of surgical robot has the potentials to improve surgical efficiency.
DDIT: Semantic Scene Completion via Deformable Deep Implicit Templates
It abstracts the shape prior of a category, and thus can provide constraints on the overall shape of an instance.
OmniVidar: Omnidirectional Depth Estimation From Multi-Fisheye Images
Estimating depth from four large field of view (FoV) cameras has been a difficult and understudied problem.
Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging
As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy.
StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS
To further improve the performance of the stereo framework, StereoPose is equipped with a parallax attention module for stereo feature fusion and an epipolar loss for improving the stereo-view consistency of network predictions.
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking
To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model.
Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking
Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances.
Deterministic Point Cloud Registration via Novel Transformation Decomposition
Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF).
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning
Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution.
Learning To Identify Correct 2D-2D Line Correspondences on Sphere
In contrast, we propose the first approach suitable for both structured and unstructured scenes.
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video
To greatly increase the label efficiency, we explore a new problem, i. e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame.
Learning Icosahedral Spherical Probability Map Based on Bingham Mixture Model for Vanishing Point Estimation
Based on this map, we can detect all the VPs.
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery
Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery.
Ranked #1 on
Action Segmentation
on JIGSAWS
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc.
Self-supervised Video Representation Learning by Pace Prediction
This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction.
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
Learning a good 3D human pose representation is important for human pose related tasks, e. g. human 3D pose estimation and action recognition.
Ranked #14 on
Self-supervised Skeleton-based Action Recognition
on NTU RGB+D
(Accuracy (XView) metric)
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology
In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance.
Robotics Human-Computer Interaction
Assembly of randomly placed parts realized by using only one robot arm with a general parallel-jaw gripper
The proposal in this paper is verified by a simulated assembly in which a robot arm completed the assembly process including parts picking from bin and a subsequent peg-in-hole assembly.
SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles
Place recognition and loop-closure detection are main challenges in the localization, mapping and navigation tasks of self-driving vehicles.
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics
We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach.
Ranked #47 on
Self-Supervised Action Recognition
on HMDB51
Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
This paper presents an efficient neural network model to generate robotic grasps with high resolution images.
Robotics
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments.
Ranked #5 on
3D Place Recognition
on CS-Campus3D
