Fed3D is a federated 3D object detection system using local-global class-aware loss for heterogeneity and prompt modules for low-bandwidth communication, claiming better performance than prior methods on limited local data.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
dataset 1polarities
use dataset 1representative citing papers
A semi-dense image matching pipeline adds scale adaptability via score-matrix hints at the coarse stage and local flow consistency via gradient loss at the fine stage.
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.
citing papers explorer
-
Fed3D: Federated 3D Object Detection
Fed3D is a federated 3D object detection system using local-global class-aware loss for heterogeneity and prompt modules for low-bandwidth communication, claiming better performance than prior methods on limited local data.
-
Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency
A semi-dense image matching pipeline adds scale adaptability via score-matrix hints at the coarse stage and local flow consistency via gradient loss at the fine stage.
-
3D Generation for Embodied AI and Robotic Simulation: A Survey
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.