73 citations · 232 across the 24 of their papers we have counts for
24 papers
Concavity-Induced Distance for Unoriented Point Cloud Decomposition
Ruoyu Wang, Yanfei Xue, Bharath Surianarayanan +2
We propose Concavity-induced Distance (CID) as a novel way to measure the dissimilarity between a pair of points in an unoriented point cloud. CID indicates the likelihood of two p…
Robust Collaborative 3D Object Detection in Presence of Pose Errors
Yifan Lu, Quanhao Li, Baoan Liu +4
Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. H…
Uncertainty Quantification of Collaborative Detection for Self-Driving
Sanbao Su, Yiming Li, Sihong He +4
Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still…
Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data
Chao Chen, Zegang Cheng, Xinhao Liu +4
Visual place recognition (VPR) using deep networks has achieved state-of-the-art performance. However, most of them require a training set with ground truth sensor poses to obtain…
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
Yiming Li, Dekun Ma, Ziyan An +4
Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally imp…
A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks
Yefan Zhou, Yiru Shen, Yujun Yan +2
Neural networks (NN) for single-view 3D reconstruction (SVR) have gained in popularity. Recent work points out that for SVR, most cutting-edge NNs have limited performance on recon…
Learning Distilled Collaboration Graph for Multi-Agent Perception
Yiming Li, Shunli Ren, Pengxiang Wu +3
To promote better performance-bandwidth trade-off for multi-agent perception, we propose a novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and adap…
Projector-Guided Non-Holonomic Mobile 3D Printing
Xuchu Xu, Ziteng Wang, Chen Feng
Fused deposition modeling (FDM) using mobile robots instead of the gantry-based 3D printer enables additive manufacturing at a larger scale with higher speed. This introduces chall…
Deep Weakly Supervised Positioning
Ruoyu Wang, Xuchu Xu, Li Ding +2
PoseNet can map a photo to the position where it is taken, which is appealing in robotics. However, training PoseNet requires full supervision, where ground truth positions are non…
Self-supervised Spatial Reasoning on Multi-View Line Drawings
Siyuan Xiang, Anbang Yang, Yanfei Xue +2
Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. Based on the fac…
Simultaneous Navigation and Construction Benchmarking Environments
Wenyu Han, Chen Feng, Haoran Wu +5
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major r…
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
Yiming Li, Congcong Wen, Felix Juefei-Xu +1
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions. When autonomous vehicl…
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Abhinav Kumar, Tim K. Marks, Wenxuan Mou +6
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted loca…
SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings
Wenyu Han, Siyuan Xiang, Chenhui Liu +2
Spatial reasoning is an important component of human intelligence. We can imagine the shapes of 3D objects and reason about their spatial relations by merely looking at their three…
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
Duanshun Li, Jing Liu, Noseong Park +7
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc. In this paper, we present a deep learning technique-based method to solve large-s…
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
Siheng Chen, Chaojing Duan, Yaoqing Yang +3
We propose a deep autoencoder with graph topology inference and filtering to achieve compact representations of unorganized 3D point clouds in an unsupervised manner. Many previous…
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
Li Ding, Chen Feng
We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent…
Primitive Fitting Using Deep Boundary Aware Geometric Segmentation
Duanshun Li, Chen Feng
To identify and fit geometric primitives (e.g., planes, spheres, cylinders, cones) in a noisy point cloud is a challenging yet beneficial task for fields such as robotics and rever…
Simultaneous Edge Alignment and Learning
Zhiding Yu, Weiyang Liu, Yang Zou +4
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to c…
VLASE: Vehicle Localization by Aggregating Semantic Edges
Xin Yu, Sagar Chaturvedi, Chen Feng +4
In this paper, we propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pa…
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Yaoqing Yang, Chen Feng, Yiru Shen +1
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and…
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Yiru Shen, Chen Feng, Yaoqing Yang +1
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieve…
Detecting and Grouping Identical Objects for Region Proposal and Classification
Wim Abbeloos, Sergio Caccamo, Esra Ataer-Cansizoglu +3
Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify s…
CASENet: Deep Category-Aware Semantic Edge Detection
Zhiding Yu, Chen Feng, Ming-Yu Liu +1
Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation.…