5 citations · 8 across the 11 of their papers we have counts for
13 papers
Scaling Discovery Requires Forgetting
Shangyin Tan, Ben Zhou, Lakshya A Agrawal +8
Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new idea…
SurroundNEXO: Ego-Centric Metric Bridging for Spatially Consistent Geometry in Autonomous Driving
Shuai Yuan, Runxi Tang, Yuzhou Ji +8
Modern autonomous driving depends on accurate metric 3D understanding for perception, reconstruction, and planning, which in turn requires reliable multi-camera depth prediction. H…
LiDAR Prompted Spatio-Temporal Multi-View Stereo for Autonomous Driving
Qihao Sun, Jiarun Liu, Ziqian Ni +5
Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consist…
GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving
Chunyong Hu, Qi Luo, Jianyun Xu +3
In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to…
SAM4D: Segment Anything in Camera and LiDAR Streams
Jianyun Xu, Song Wang, Ziqian Ni +4
We present SAM4D, a multi-modal and temporal foundation model designed for promptable segmentation across camera and LiDAR streams. Unified Multi-modal Positional Encoding (UMPE) i…
PixelThink: Towards Efficient Chain-of-Pixel Reasoning
Song Wang, Gongfan Fang, Lingdong Kong +6
Existing reasoning segmentation approaches typically fine-tune multimodal large language models (MLLMs) using image-text pairs and corresponding mask labels. However, they exhibit…
PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning
Song Wang, Xiaolu Liu, Lingdong Kong +6
Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained mode…
Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation
Xianming Zeng, Sicong Du, Qifeng Chen +13
Sensor simulation is pivotal for scalable validation of autonomous driving systems, yet existing Neural Radiance Fields (NeRF) based methods face applicability and efficiency chall…
FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection
Chunyong Hu, Hang Zheng, Kun Li +11
Multi-sensor modal fusion has demonstrated strong advantages in 3D object detection tasks. However, existing methods that fuse multi-modal features require transforming features in…
PUPS: Point Cloud Unified Panoptic Segmentation
Shihao Su, Jianyun Xu, Huanyu Wang +4
Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points. Previous…
INT: Towards Infinite-frames 3D Detection with An Efficient Framework
Jianyun Xu, Zhenwei Miao, Da Zhang +7
It is natural to construct a multi-frame instead of a single-frame 3D detector for a continuous-time stream. Although increasing the number of frames might improve performance, pre…
CenterAtt: Fast 2-stage Center Attention Network
Jianyun Xu, Xin Tang, Jian Dou +2
In this technical report, we introduce the methods of HIKVISION_LiDAR_Det in the challenge of waymo open dataset real-time 3D detection. Our solution for the competition are built…
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
Jianyun Xu, Ruixiang Zhang, Jian Dou +3
Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometric…