74 citations · 196 across the 26 of their papers we have counts for
32 papers
Fiducial Exoskeletons: Image-Centric Robot State Estimation
Cameron Smith, Basile Van Hoorick, Vitor Guizilini +1
We introduce Fiducial Exoskeletons, an image-based reformulation of 3D robot state estimation that replaces cumbersome procedures and motor-centric pipelines with single-image infe…
SIRE: SE(3) Intrinsic Rigidity Embeddings
Cameron Smith, Basile Van Hoorick, Vitor Guizilini +1
Motion serves as a powerful cue for scene perception and understanding by separating independently moving surfaces and organizing the physical world into distinct entities. We intr…
Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion
Vitor Guizilini, Muhammad Zubair Irshad, Dian Chen +2
Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-vi…
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion
Vitor Guizilini, Pavel Tokmakov, Achal Dave +1
3D reconstruction from a single image is a long-standing problem in computer vision. Learning-based methods address its inherent scale ambiguity by leveraging increasingly large la…
Incorporating dense metric depth into neural 3D representations for view synthesis and relighting
Arkadeep Narayan Chaudhury, Igor Vasiljevic, Sergey Zakharov +4
Synthesizing accurate geometry and photo-realistic appearance of small scenes is an active area of research with compelling use cases in gaming, virtual reality, robotic-manipulati…
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini +1
Monocular visual odometry is a key technology in various autonomous systems. Traditional feature-based methods suffer from failures due to poor lighting, insufficient texture, and…
Towards Realistic Scene Generation with LiDAR Diffusion Models
Haoxi Ran, Vitor Guizilini, Yue Wang
Diffusion models (DMs) excel in photo-realistic image synthesis, but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating…
Robust Self-Supervised Extrinsic Self-Calibration
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini +2
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth esti…
Towards Zero-Shot Scale-Aware Monocular Depth Estimation
Vitor Guizilini, Igor Vasiljevic, Dian Chen +2
Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions. Even so, the resulting models will be geometry-specific, with lear…
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate ge…
Depth Is All You Need for Monocular 3D Detection
Dennis Park, Jie Li, Dian Chen +2
A key contributor to recent progress in 3D detection from single images is monocular depth estimation. Existing methods focus on how to leverage depth explicitly, by generating pse…
Depth Field Networks for Generalizable Multi-view Scene Representation
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matchi…
Multi-Frame Self-Supervised Depth with Transformers
Vitor Guizilini, Rares Ambrus, Dian Chen +2
Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearanc…
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels
Vitor Guizilini, Kuan-Hui Lee, Rares Ambrus +1
Self-supervised monocular depth estimation enables robots to learn 3D perception from raw video streams. This scalable approach leverages projective geometry and ego-motion to lear…
Self-Supervised Camera Self-Calibration from Video
Jiading Fang, Igor Vasiljevic, Vitor Guizilini +4
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration i…
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
Yue Wang, Vitor Guizilini, Tianyuan Zhang +3
We introduce a framework for multi-camera 3D object detection. In contrast to existing works, which estimate 3D bounding boxes directly from monocular images or use depth predictio…
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Dennis Park, Rares Ambrus, Vitor Guizilini +2
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. These…
Full Surround Monodepth from Multiple Cameras
Vitor Guizilini, Igor Vasiljevic, Rares Ambrus +2
Self-supervised monocular depth and ego-motion estimation is a promising approach to replace or supplement expensive depth sensors such as LiDAR for robotics applications like auto…
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
Vitor Guizilini, Rares Ambrus, Wolfram Burgard +1
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from…
Geometric Unsupervised Domain Adaptation for Semantic Segmentation
Vitor Guizilini, Jie Li, Rares Ambrus +1
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a d…
Monocular Depth Estimation for Soft Visuotactile Sensors
Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy +3
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 hi…
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
Igor Vasiljevic, Vitor Guizilini, Rares Ambrus +4
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. However, one significant lim…
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Vitor Guizilini, Rui Hou, Jie Li +2
Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning re…
Neural Outlier Rejection for Self-Supervised Keypoint Learning
Jiexiong Tang, Hanme Kim, Vitor Guizilini +2
Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms. Recently, several learned keypoint methods have demonstra…