32 citations · 55 across the 20 of their papers we have counts for
23 papers
Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis
Xiang Xu, Alan Liang, Youquan Liu +5
Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capacity across all spatial regio…
SpatialBench: Is Your Spatial Foundation Model an All-Round Player?
Haosong Peng, Hao Li, Jiaqi Chen +10
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing…
From Pixels to Words -- Towards Native One-Vision Models at Scale
Haiwen Diao, Jiahao Wang, Penghao Wu +18
Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular framework that inevitably fragmen…
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
Xiang An, Yin Xie, Feilong Tang +27
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of mu…
PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects
Ziang Cao, Yinghao Liu, Haitian Li +5
Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods eithe…
AI for Auto-Research: Roadmap & User Guide
Lingdong Kong, Xian Sun, Wei Chow +17
AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draf…
OnlineSI: Taming Large Language Model for Online 3D Understanding and Grounding
Zixian Liu, Zhaoxi Chen, Liang Pan +1
In recent years, researchers have increasingly been interested in how to enable Multimodal Large Language Models (MLLM) to possess spatial understanding and reasoning capabilities.…
U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences
Xiang Xu, Alan Liang, Youquan Liu +4
Modeling dynamic 3D environments from LiDAR sequences is central to building reliable 4D worlds for autonomous driving and embodied AI. Existing generative frameworks, however, oft…
3EED: Ground Everything Everywhere in 3D
Rong Li, Yuhao Dong, Tianshuai Hu +7
Visual grounding in 3D is the key for embodied agents to localize language-referred objects in open-world environments. However, existing benchmarks are limited to indoor focus, si…
VideoLucy: Deep Memory Backtracking for Long Video Understanding
Jialong Zuo, Yongtai Deng, Lingdong Kong +7
Recent studies have shown that agent-based systems leveraging large language models (LLMs) for key information retrieval and integration have emerged as a promising approach for lo…
See4D: Pose-Free 4D Generation via Auto-Regressive Video Inpainting
Dongyue Lu, Ao Liang, Tianxin Huang +8
Immersive applications call for synthesizing spatiotemporal 4D content from casual videos without costly 3D supervision. Existing video-to-4D methods typically rely on manually ann…
3D and 4D World Modeling: A Survey
Lingdong Kong, Yu Yang, Jianbiao Mei +20
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphas…
Enhanced Spatiotemporal Consistency for Image-to-LiDAR Data Pretraining
Xiang Xu, Lingdong Kong, Hui Shuai +5
LiDAR representation learning has emerged as a promising approach to reducing reliance on costly and labor-intensive human annotations. While existing methods primarily focus on sp…
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Shaoyuan Xie, Lingdong Kong, Yuhao Dong +5
Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use for autonomous driving, particularly in generating interpretable driving decisions through n…
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Lingdong Kong, Xiang Xu, Youquan Liu +6
Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous drivi…
LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Xiang Xu, Lingdong Kong, Hui Shuai +3
LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, existing methods predominantly focu…
DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes
Hengwei Bian, Lingdong Kong, Haozhe Xie +3
Urban scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dyna…
Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation
Xiaoyang Wu, Xiang Xu, Lingdong Kong +5
In this technical report, we detail our first-place solution for the 2024 Waymo Open Dataset Challenge's semantic segmentation track. We significantly enhanced the performance of P…
4D Contrastive Superflows are Dense 3D Representation Learners
Xiang Xu, Lingdong Kong, Hui Shuai +5
In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations -- a process that is both costly…
Multi-Space Alignments Towards Universal LiDAR Segmentation
Youquan Liu, Lingdong Kong, Xiaoyang Wu +5
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of…
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving
Shaoyuan Xie, Lingdong Kong, Wenwei Zhang +4
Recent advancements in bird's eye view (BEV) representations have shown remarkable promise for in-vehicle 3D perception. However, while these methods have achieved impressive resul…
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
Lingdong Kong, Xiang Xu, Jiawei Ren +5
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supe…
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
Lingdong Kong, Xiang Xu, Jun Cen +4
Safety-critical 3D scene understanding tasks necessitate not only accurate but also confident predictions from 3D perception models. This study introduces Calib3D, a pioneering eff…