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Found 8 papers, 4 papers with code
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend.
Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization
Image retrieval has been employed as a robust complementary technique to address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning.
OscNet: Machine Learning on CMOS Oscillator Networks
In this context, we propose a new and energy efficient machine learning framework implemented on CMOS Oscillator Networks (OscNet).
Uncertainty Quantification in Stereo Matching
Stereo matching plays a crucial role in various applications, where understanding uncertainty can enhance both safety and reliability.
SpatialBot: Precise Spatial Understanding with Vision Language Models
Vision Language Models (VLMs) have achieved impressive performance in 2D image understanding, however they are still struggling with spatial understanding which is the foundation of Embodied AI.
Ranked #4 on
Spatial Reasoning
on 6-DoF SpatialBench
Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects
To clearly reconstruct dynamic scenes, we propose a new framework by considering two frames at a time. We pretrain a NeRF model for an articulated object. When articulated objects moves, Knowledge NeRF learns to generate novel views at the new state by incorporating past knowledge in the pretrained NeRF model with minimal observations in the present state.
Object-level Geometric Structure Preserving for Natural Image Stitching
The topic of stitching images with globally natural structures holds paramount significance, with two main goals: pixel-level alignment and distortion prevention.
VDD: Varied Drone Dataset for Semantic Segmentation
Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground.
Ranked #1 on
Semantic Segmentation
on VDD
