no code implementations • 18 Jan 2024 • René Zurbrügg, Yifan Liu, Francis Engelmann, Suryansh Kumar, Marco Hutter, Vaishakh Patil, Fisher Yu
Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps.
no code implementations • 8 Nov 2023 • Nishant Jain, Suryansh Kumar, Luc van Gool
The key ideas presented in this paper are (i) Recovering accurate camera parameters via a robust pipeline from unposed day-to-day images is equally crucial in neural novel view synthesis problem; (ii) It is rather more practical to model object's content at different resolutions since dramatic camera motion is highly likely in day-to-day unposed images.
no code implementations • 26 May 2023 • Junting Chen, Guohao Li, Suryansh Kumar, Bernard Ghanem, Fisher Yu
Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
no code implementations • 27 Apr 2023 • Yasaman Haghighi, Suryansh Kumar, Jean-Philippe Thiran, Luc van Gool
Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment using a camera sensor while simultaneously tracking its position over time.
no code implementations • 18 Apr 2023 • Han Yao Choong, Suryansh Kumar, Luc van Gool
As a result, in this work, we take the privilege to perform an early exploration of applying a quantum computing algorithm to this important image enhancement problem, i. e., SISR.
no code implementations • CVPR 2023 • Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc van Gool
Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution.
no code implementations • CVPR 2023 • Nishant Jain, Suryansh Kumar, Luc van Gool
Extensive evaluation of our approach on the popular benchmark dataset, such as Tanks and Temples, shows substantial improvement in view synthesis results compared to the prior art.
2 code implementations • 13 Feb 2023 • Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc van Gool
While state-of-the-art deep neural network methods for SIDP learn the scene depth from images in a supervised setting, they often overlook the invaluable invariances and priors in the rigid scene space, such as the regularity of the scene.
Ranked #18 on Monocular Depth Estimation on NYU-Depth V2
no code implementations • 1 Feb 2023 • Weirong Chen, Suryansh Kumar, Fisher Yu
This work introduces an effective and practical solution to the dense two-view structure from motion (SfM) problem.
no code implementations • 2 Dec 2022 • Tobias Fischer, Yung-Hsu Yang, Suryansh Kumar, Min Sun, Fisher Yu
To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings.
no code implementations • 14 Oct 2022 • Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc van Gool
The proposed approach in this paper exploits the benefit of uncertainty modeling in a deep neural network for a reliable fusion of photometric stereo (PS) and multi-view stereo (MVS) network predictions.
no code implementations • 9 Oct 2022 • Nishant Jain, Suryansh Kumar, Luc van Gool
Although recently proposed Mip-NeRF could handle multi-scale imaging problems with NeRF, it cannot handle camera pose estimation error.
no code implementations • 17 Sep 2022 • Soomin Lee, Le Chen, Jiahao Wang, Alexander Liniger, Suryansh Kumar, Fisher Yu
In this paper, we tackle the problem of active robotic 3D reconstruction of an object.
no code implementations • 13 Jul 2022 • Suryansh Kumar, Luc van Gool
Besides that, the paper provides insights into the NRSfM factorization -- both in terms of shape and motion -- and is the first approach to show the benefit of single rotation averaging for NRSfM.
1 code implementation • 7 Apr 2022 • Erik Sandström, Martin R. Oswald, Suryansh Kumar, Silvan Weder, Fisher Yu, Cristian Sminchisescu, Luc van Gool
Multi-sensor depth fusion is able to substantially improve the robustness and accuracy of 3D reconstruction methods, but existing techniques are not robust enough to handle sensors which operate with diverse value ranges as well as noise and outlier statistics.
no code implementations • CVPR 2022 • Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc van Gool
At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure.
no code implementations • 11 Oct 2021 • Francesco Sarno, Suryansh Kumar, Berk Kaya, Zhiwu Huang, Vittorio Ferrari, Luc van Gool
We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network.
no code implementations • 11 Oct 2021 • Berk Kaya, Suryansh Kumar, Francesco Sarno, Vittorio Ferrari, Luc van Gool
Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
1 code implementation • 11 Aug 2021 • Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstrom, Cristian Sminchisescu, Luc van Gool
This paper presents a real-time online vision framework to jointly recover an indoor scene's 3D structure and semantic label.
no code implementations • CVPR 2022 • Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc van Gool
The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows.
no code implementations • 17 Jan 2021 • Yan Wu, Zhiwu Huang, Suryansh Kumar, Rhea Sanjay Sukthanker, Radu Timofte, Luc van Gool
Modern solutions to the single image super-resolution (SISR) problem using deep neural networks aim not only at better performance accuracy but also at a lighter and computationally efficient model.
no code implementations • CVPR 2021 • Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc van Gool
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem.
1 code implementation • 27 Oct 2020 • Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Erik Goron Endsjo, Yan Wu, Luc van Gool
To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design.
no code implementations • 15 Jun 2020 • Suryansh Kumar, Luc van Gool, Carlos E. P. de Oliveira, Anoop Cherian, Yuchao Dai, Hongdong Li
Assuming that a deforming shape is composed of a union of local linear subspace and, span a global low-rank space over multiple frames enables us to efficiently model complex non-rigid deformations.
no code implementations • 19 Nov 2019 • Suryansh Kumar, Yuchao Dai, Hongdong Li
We assume that a dynamic scene can be approximated by numerous piecewise planar surfaces, where each planar surface enjoys its own rigid motion, and the global change in the scene between two frames is as-rigid-as-possible (ARAP).
no code implementations • 27 Feb 2019 • Suryansh Kumar
The benefit of this work lies in its simplicity of implementation, strong theoretical justification to the motion and structure estimation, and its invincible originality.
no code implementations • 11 Feb 2019 • Suryansh Kumar, Ram Srivatsav Ghorakavi, Yuchao Dai, Hongdong Li
Given per-pixel optical flow correspondences between two consecutive frames and, the sparse depth prior for the reference frame, we show that, we can effectively recover the dense depth map for the successive frames without solving for 3D motion parameters.
no code implementations • CVPR 2019 • Suryansh Kumar
These Grassmann points in the lower-dimension then act as a representative for the selection of high-dimensional Grassmann samples to perform each local reconstruction.
no code implementations • CVPR 2018 • Suryansh Kumar, Anoop Cherian, Yuchao Dai, Hongdong Li
To address these issues, in this paper, we propose a new approach for dense NRSfM by modeling the problem on a Grassmann manifold.
no code implementations • ICCV 2017 • Suryansh Kumar, Yuchao Dai, Hongdong Li
This paper proposes a new approach for monocular dense 3D reconstruction of a complex dynamic scene from two perspective frames.
no code implementations • 14 May 2017 • Suryansh Kumar, Yuchao Dai, Hongdong Li
This spatio-temporal representation not only provides competitive 3D reconstruction but also outputs robust segmentation of multiple non-rigid objects.
no code implementations • 15 Jul 2016 • Suryansh Kumar, Yuchao Dai, Hongdong Li
Recent progress have extended SFM to the areas of {multi-body SFM} (where there are {multiple rigid} relative motions in the scene), as well as {non-rigid SFM} (where there is a single non-rigid, deformable object or scene).