Search Results for author:
Found 11 papers, 8 papers with code
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Our proposed GSmoothFace model mainly consists of the Audio to Expression Prediction (A2EP) module and the Target Adaptive Face Translation (TAFT) module.
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge Transfer
The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences.
LATR: 3D Lane Detection from Monocular Images with Transformer
On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information.
Ranked #1 on
3D Lane Detection
on OpenLane
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Due to the motion-centric nature, our method shows its impressive generalizability with limited training labels and provides good differentiability for end-to-end cycle training.
Benchmarking the Robustness of LiDAR Semantic Segmentation Models
In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions.
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i. e., rendered or projected 2D images of the 3D object, to boost point cloud analysis.
Ranked #12 on
3D Point Cloud Classification
on ModelNet40
M$^2$-3DLaneNet: Exploring Multi-Modal 3D Lane Detection
Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature.
Ranked #5 on
3D Lane Detection
on OpenLane
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion.
Ranked #4 on
Robust 3D Semantic Segmentation
on nuScenes-C
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving.
Ranked #1 on
Object Tracking
on KITTI
Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds
Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area.
Ranked #2 on
Object Tracking
on KITTI
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise.
Ranked #27 on
Semantic Segmentation
on S3DIS
