Search Results for author:
Found 30 papers, 16 papers with code
Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation
This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation.
Ranked #1 on
3D Semantic Segmentation
on SemanticKITTI
LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors
We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using a single LiDAR and 4 IMUs.
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes.
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds
Quantitative and qualitative experiments show that our method outperforms the techniques based only on RGB images.
Towards 3D Scene Understanding by Referring Synthetic Models
Promising performance has been achieved for visual perception on the point cloud.
Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism
In this paper, we propose a self-supervised point cloud completion method (TraPCC) for vehicles in real traffic scenes without any complete data.
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR
We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments.
NIMBLE: A Non-rigid Hand Model with Bones and Muscles
Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world.
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach
Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline.
Referring Self-supervised Learning on 3D Point Cloud
In this paper, we study a new problem named Referring Self-supervised Learning (RSL) on 3D scene understanding: Given the 3D synthetic models with labels and the unlabeled 3D real scene scans, our goal is to distinguish the identical semantic objects on an unseen scene according to the referring synthetic 3D models.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception
In this paper, we benchmark our model on these three tasks.
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation
Due to the information sparsity of local cost volume, we further introduce match reweighting and structure-aware attention, to make the depth information more concentrated.
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
Autonomous Driving
Monocular Cross-View Road Scene Parsing(Road)
+1
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods.
SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos
In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
Input-Output Balanced Framework for Long-tailed LiDAR Semantic Segmentation
A thorough and holistic scene understanding is crucial for autonomous vehicles, where LiDAR semantic segmentation plays an indispensable role.
ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References
We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner.
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures
Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.
Ranked #3 on
3D Semantic Segmentation
on SemanticKITTI
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space.
Ranked #7 on
LIDAR Semantic Segmentation
on nuScenes
AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points
Then we aggregate dynamic points to instance points, which stand for moving objects such as pedestrians in videos.
CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception
This paper reviews the CVPR 2019 challenge on Autonomous Driving.
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds.
Decision Propagation Networks for Image Classification
High-level (e. g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e. g., color) features in the early layers underexplored.
Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms
Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).
Ranked #1 on
Trajectory Prediction
on Apolloscape Trajectory
Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT
We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes.
