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Found 5 papers, 2 papers with code
Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
Domain adaptation is an important task to enable learning when labels are scarce.
Ranked #2 on
Continual Learning
on Cifar100 (20 tasks)
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation.
Multi-view PointNet for 3D Scene Understanding
Fusion of 2D images and 3D point clouds is important because information from dense images can enhance sparse point clouds.
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.).
Ranked #9 on
Depth Completion
on KITTI Depth Completion
End-to-End Race Driving with Deep Reinforcement Learning
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding).
