Search Results for author:
Found 20 papers, 7 papers with code
Multi-Frame Self-Supervised Depth with Transformers
Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures.
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels
However, the simultaneous self-supervised learning of depth and scene flow is ill-posed, as there are infinitely many combinations that result in the same 3D point.
Self-Supervised Camera Self-Calibration from Video
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.
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 #13 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)
Full Surround Monodepth from Multiple Cameras
In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs.
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars.
Geometric Unsupervised Domain Adaptation for Semantic Segmentation
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation.
Monocular Depth Estimation for Soft Visuotactile Sensors
Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key challenges for robust manipulation, as they enable reliable grasps along with the ability to obtain high-resolution sensory feedback on contact geometry and forces.
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association.
Driving Through Ghosts: Behavioral Cloning with False Positives
In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative.
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
Self-supervised learning has emerged as a powerful tool for depth and ego-motion estimation, leading to state-of-the-art results on benchmark datasets.
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions.
Neural Outlier Rejection for Self-Supervised Keypoint Learning
By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion.
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances
Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks.
Two Stream Networks for Self-Supervised Ego-Motion Estimation
Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues.
3D Packing for Self-Supervised Monocular Depth Estimation
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Both contributions provide significant performance gains over the state-of-the-art in self-supervised depth and pose estimation on the public KITTI benchmark.
Unsupervised Object Discovery and Segmentation of RGBD-images
In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images.
