85 citations · 157 across the 21 of their papers we have counts for
19 papers · 1 filter
PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet
Xiaopei Wu, Chenshu Hou, Liang Peng +9
3D dense captioning, an emerging vision-language task, aims to generate descriptive sentences for each object in the 3D scene. Despite the impressive results achieved by previous m…
Heterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation
Xiaopei Wu, Yuenan Hou, Junkai Xu +7
Multi-modal fusion and multi-model ensembling are prevalent in enhancing the performance of 3D semantic segmentation. Despite the impressive performance, these methods either rely…
PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding
Chenshu Hou, Liang Peng, Xiaopei Wu +2
3D visual grounding aims to identify objects in 3D point cloud scenes that match specific natural language descriptions. This requires the model to not only focus on the target obj…
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Zhanwei Zhang, Minghao Chen, Shuai Xiao +7
Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels,…
G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction
Zhanwei Zhang, Zishuo Hua, Minghao Chen +4
Predicting future trajectories of traffic agents accurately holds substantial importance in various applications such as autonomous driving. Previous methods commonly infer all fut…
Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving
Junkai Xu, Liang Peng, Haoran Cheng +6
Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera…
Few-shot Hybrid Domain Adaptation of Image Generators
Hengjia Li, Yang Liu, Linxuan Xia +7
Can a pre-trained generator be adapted to the hybrid of multiple target domains and generate images with integrated attributes of them? In this work, we introduce a new task -- Few…
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
Junkai Xu, Liang Peng, Haoran Cheng +5
In the field of monocular 3D detection, it is common practice to utilize scene geometric clues to enhance the detector's performance. However, many existing works adopt these clues…
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation
Minghao Chen, Zepeng Gao, Shuai Zhao +4
Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set…
SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition
Qibo Qiu, Wenxiao Wang, Haochao Ying +3
Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattere…
Learning Occupancy for Monocular 3D Object Detection
Liang Peng, Junkai Xu, Haoran Cheng +6
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to fac…
PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer
Honghui Yang, Wenxiao Wang, Minghao Chen +5
Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling whil…
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
Hengjia Li, Tu Zheng, Zhihao Chi +5
Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to dir…
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention
Wenxiao Wang, Wei Chen, Qibo Qiu +5
While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first pro…
General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment
Liang Xie, Yibo Yang, Wenxiao Wang +4
Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformati…
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection
Chenxi Huang, Tong He, Haidong Ren +3
Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap betw…
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
Yuqi Lin, Minghao Chen, Wenxiao Wang +5
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training cos…
Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph
Honghui Yang, Zili Liu, Xiaopei Wu +4
Two-stage detectors have gained much popularity in 3D object detection. Most two-stage 3D detectors utilize grid points, voxel grids, or sampled keypoints for RoI feature extractio…
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
Wenxiao Wang, Lu Yao, Long Chen +4
Transformers have made great progress in dealing with computer vision tasks. However, existing vision transformers do not yet possess the ability of building the interactions among…