146 citations · 202 across the 14 of their papers we have counts for
17 papers
PhysWAM: Physically Consistent World Action Model for Autonomous Driving
Dhruv Parikh, Fengcheng Yu, Quankai Gao +11
World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constr…
DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models
Fengcheng Yu, Dhruv Parikh, Junjie Ye +5
Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expert imitation. Yet imitation p…
FastMap: Revisiting Structure from Motion through First-Order Optimization
Jiahao Li, Haochen Wang, Muhammad Zubair Irshad +4
We propose FastMap, a new global structure from motion method focused on speed and simplicity. Previous methods like COLMAP and GLOMAP are able to estimate high-precision camera po…
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…
Transcrib3D: 3D Referring Expression Resolution through Large Language Models
Jiading Fang, Xiangshan Tan, Shengjie Lin +6
If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expres…
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…
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
Jiading Fang, Shengjie Lin, Igor Vasiljevic +5
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small ne…
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…
Neural Camera Models
Igor Vasiljevic
Modern computer vision has moved beyond the domain of internet photo collections and into the physical world, guiding camera-equipped robots and autonomous cars through unstructure…
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…
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…
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…
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…
DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang +8
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements. DIODE (Dense Indoor/Outdoor DEpth…
Examining the Impact of Blur on Recognition by Convolutional Networks
Igor Vasiljevic, Ayan Chakrabarti, Gregory Shakhnarovich
State-of-the-art algorithms for many semantic visual tasks are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annot…