most citedEfficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation

2 citations · 4 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI20241 cited

VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents

Xiao Liu, Tianjie Zhang, Yu Gu +27

Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agent…

cs.CL2024180 cited

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Team GLM, :, Aohan Zeng +56

We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes…

cs.CV2023

Forward Flow for Novel View Synthesis of Dynamic Scenes

Xiang Guo, Jiadai Sun, Yuchao Dai +6

This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of dynamic scenes using forward warping. Existing methods often adopt a static NeRF to represen…

cs.CV20232 cited

Digging into Depth Priors for Outdoor Neural Radiance Fields

Chen Wang, Jiadai Sun, Lina Liu +5

Neural Radiance Fields (NeRF) have demonstrated impressive performance in vision and graphics tasks, such as novel view synthesis and immersive reality. However, the shape-radiance…

cs.CV2023

MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation

Chenming Wu, Jiadai Sun, Zhelun Shen +1

Simulating camera sensors is a crucial task in autonomous driving. Although neural radiance fields are exceptional at synthesizing photorealistic views in driving simulations, they…

cs.CV20222 cited

Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation

Jiadai Sun, Yuchao Dai, Xianjing Zhang +4

Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path p…