Search Results for author:
Found 23 papers, 10 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.
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
This top-down approach outperforms its bottom-up counterpart in which object bounding box prediction follows per-pixel depth estimation, since it does not suffer from the compounding error introduced by a depth prediction model.
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)
MarioNette: Self-Supervised Sprite Learning
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters.
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.
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.
Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels.
Real-Time Panoptic Segmentation from Dense Detections
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.
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.
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.
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.
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps
This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions.
Learning to Race through Coordinate Descent Bayesian Optimisation
On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system.
Continuous Convolutional Neural Networks for Image Classification
This paper introduces the concept of continuous convolution to neural networks and deep learning applications in general.
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform.
