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Found 8 papers, 1 papers with code
Scene Synthesis from Human Motion
Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly.
What You Can Reconstruct From a Shadow
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Depth Is All You Need for Monocular 3D Detection
Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors.
Shadows Shed Light on 3D Objects
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Revealing Occlusions with 4D Neural Fields
For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence.
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets.
Ranked #19 on
Semantic Segmentation
on NYU Depth v2
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.
Ranked #1 on
Monocular 3D Object Detection
on KITTI Pedestrian Hard
(using extra training data)
Exploring Weak Stabilization for Motion Feature Extraction
We describe novel but simple motion features for the problem of detecting objects in video sequences.
