Search Results for author:
Found 18 papers, 14 papers with code
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
Existing PLMs generate protein sequences based on a single-condition constraint from a specific modality, struggling to simultaneously satisfy multiple constraints across different modalities.
OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving
To address the needs of object-aware tasks in 3D perception, we introduce OLiDM, a novel framework capable of generating high-fidelity LiDAR data at both the object and the scene levels.
ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction
In this work, we strive to improve performance by introducing a series of targeted improvements for 3D semantic occupancy prediction and flow estimation.
RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational inefficiencies and feature competition between tasks when using traditional multi-task learning approaches.
Ranked #6 on
3D Lane Detection
on OpenLane
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities.
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection
Specifically, DMA builds a domain-mixing 3D instance bank for the teacher and student models during training, resulting in aligned data representation.
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
To bridge this gap, we propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth feature representation in two aspects.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving
In this work, we propose a new multi-modal visual grounding task, termed LiDAR Grounding.
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i. e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models.
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e. g., from Waymo (64-beam) to nuScenes (32-beam).
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds
In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i. e., the point cloud videos.
Semi-supervised 3D Object Detection with Proficient Teachers
To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed.
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination.
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module.
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed.
Ranked #20 on
Monocular 3D Object Detection
on KITTI Cars Moderate
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences.
A Unified Object Motion and Affinity Model for Online Multi-Object Tracking
In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure.
IoU Loss for 2D/3D Object Detection
In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage.
