no code implementations • 7 Sep 2017 • Rafael Oliveira, Lionel Ott, Vitor Guizilini, Fabio Ramos
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.
no code implementations • ICLR 2018 • Vitor Guizilini, Fabio Ramos
This paper introduces the concept of continuous convolution to neural networks and deep learning applications in general.
no code implementations • 17 Feb 2018 • Rafael Oliveira, Fernando H. M. Rocha, Lionel Ott, Vitor Guizilini, Fabio Ramos, Valdir Grassi Jr
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.
1 code implementation • 24 Sep 2018 • Nícolas Rosa, Vitor Guizilini, Valdir Grassi Jr
This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions.
4 code implementations • CVPR 2020 • Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, Adrien Gaidon
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
no code implementations • 4 Oct 2019 • Vitor Guizilini, Jie Li, Rares Ambrus, Sudeep Pillai, Adrien Gaidon
Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks.
no code implementations • 4 Oct 2019 • Rares Ambrus, Vitor Guizilini, Jie Li, Sudeep Pillai, Adrien Gaidon
Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues.
no code implementations • CVPR 2020 • Rui Hou, Jie Li, Arjun Bhargava, Allan Raventos, Vitor Guizilini, Chao Fang, Jerome Lynch, Adrien Gaidon
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.
no code implementations • 4 Dec 2019 • Vitor Guizilini, Ransalu Senanayake, Fabio Ramos
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels.
1 code implementation • 7 Dec 2019 • Jiexiong Tang, Rares Ambrus, Vitor Guizilini, Sudeep Pillai, Hanme Kim, Patric Jensfelt, Adrien Gaidon
Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion.
2 code implementations • ICLR 2020 • Jiexiong Tang, Hanme Kim, Vitor Guizilini, Sudeep Pillai, Rares Ambrus
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.
1 code implementation • ICLR 2020 • Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, Adrien Gaidon
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.
1 code implementation • 15 Aug 2020 • Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Wolfram Burgard, Greg Shakhnarovich, Adrien Gaidon
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.
no code implementations • 5 Jan 2021 • Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, Alex Alspach
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.
no code implementations • ICCV 2021 • Vitor Guizilini, Jie Li, Rares Ambrus, Adrien Gaidon
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation.
1 code implementation • CVPR 2021 • Vitor Guizilini, Rares Ambrus, Wolfram Burgard, Adrien Gaidon
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars.
no code implementations • 31 Mar 2021 • Vitor Guizilini, Igor Vasiljevic, Rares Ambrus, Greg Shakhnarovich, Adrien Gaidon
In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs.
1 code implementation • NeurIPS 2021 • Dmitriy Smirnov, Michael Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin Solomon
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters.
2 code implementations • ICCV 2021 • Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, Adrien Gaidon
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 Moderate (using extra training data)
1 code implementation • 13 Oct 2021 • Yue Wang, Vitor Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, Justin Solomon
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.
Ranked #7 on Robust Camera Only 3D Object Detection on nuScenes-C
no code implementations • 6 Dec 2021 • Jiading Fang, Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Greg Shakhnarovich, Adrien Gaidon, Matthew R. Walter
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.
no code implementations • 28 Mar 2022 • Vitor Guizilini, Kuan-Hui Lee, Rares Ambrus, Adrien Gaidon
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.
no code implementations • CVPR 2022 • Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, Adrien Gaidon
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.
no code implementations • 12 Jul 2022 • Colton Stearns, Davis Rempe, Jie Li, Rares Ambrus, Sergey Zakharov, Vitor Guizilini, Yanchao Yang, Leonidas J Guibas
In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.
no code implementations • 28 Jul 2022 • Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rares Ambrus, Greg Shakhnarovich, Matthew Walter, Adrien Gaidon
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 matching.
no code implementations • 5 Oct 2022 • Dennis Park, Jie Li, Dian Chen, Vitor Guizilini, Adrien Gaidon
Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors.
no code implementations • 23 Oct 2022 • Sergey Zakharov, Rares Ambrus, Vitor Guizilini, Wadim Kehl, Adrien Gaidon
In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR).
1 code implementation • CVPR 2023 • Dian Chen, Jie Li, Vitor Guizilini, Rares Ambrus, Adrien Gaidon
We design view-conditioned queries at the output level, which enables the generation of multiple virtual frames during training to learn viewpoint equivariance by enforcing multi-view consistency.
no code implementations • ICCV 2023 • Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rares Ambrus, Sergey Zakharov, Vincent Sitzmann, Adrien Gaidon
In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information.
1 code implementation • 22 May 2023 • Jiading Fang, Shengjie Lin, Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Adrien Gaidon, Gregory Shakhnarovich, Matthew R. Walter
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 neural nets instead of collections of images.
no code implementations • ICCV 2023 • Vitor Guizilini, Igor Vasiljevic, Dian Chen, Rares Ambrus, Adrien Gaidon
Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions.
no code implementations • 4 Aug 2023 • Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely.
1 code implementation • ICCV 2023 • Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus
NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference.
Ranked #1 on Generalizable Novel View Synthesis on NERDS 360
no code implementations • 21 Mar 2024 • Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rares Ambrus, Sergey Zakharov
We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image.
1 code implementation • 31 Mar 2024 • Haoxi Ran, Vitor Guizilini, Yue Wang
In this paper, we propose LiDAR Diffusion Models (LiDMs) to generate LiDAR-realistic scenes from a latent space tailored to capture the realism of LiDAR scenes by incorporating geometric priors into the learning pipeline.
no code implementations • 1 Apr 2024 • Muhammad Zubair Irshad, Sergey Zakahrov, Vitor Guizilini, Adrien Gaidon, Zsolt Kira, Rares Ambrus
Given the capabilities of neural fields in densely representing a 3D scene from 2D images, we ask the question: Can we scale their self-supervised pretraining, specifically using masked autoencoders, to generate effective 3D representations from posed RGB images.