Search Results for author:
Found 32 papers, 4 papers with code
ICGNet: A Unified Approach for Instance-Centric Grasping
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.
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images
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.
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers
Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
Neural Implicit Dense Semantic SLAM
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.
Quantum Annealing for Single Image Super-Resolution
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.
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
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.
Enhanced Stable View Synthesis
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.
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
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
Uncertainty-Driven Dense Two-View Structure from Motion
This work introduces an effective and practical solution to the dense two-view structure from motion (SfM) problem.
CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion
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.
Multi-View Photometric Stereo Revisited
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.
Robustifying the Multi-Scale Representation of Neural Radiance Fields
Although recently proposed Mip-NeRF could handle multi-scale imaging problems with NeRF, it cannot handle camera pose estimation error.
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
In this paper, we tackle the problem of active robotic 3D reconstruction of an object.
Organic Priors in Non-Rigid Structure from Motion
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.
Learning Online Multi-Sensor Depth Fusion
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.
Uncertainty-Aware Deep Multi-View Photometric Stereo
At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure.
Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo
Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
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.
A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes
This paper presents a real-time online vision framework to jointly recover an indoor scene's 3D structure and semantic label.
Generative Flows with Invertible Attentions
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.
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
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.
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem.
Neural Architecture Search of SPD Manifold Networks
To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design.
Dense Non-Rigid Structure from Motion: A Manifold Viewpoint
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.
Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene
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).
Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited
The benefit of this work lies in its simplicity of implementation, strong theoretical justification to the motion and structure estimation, and its invincible originality.
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation
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.
Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure from Motion
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.
Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective
To address these issues, in this paper, we propose a new approach for dense NRSfM by modeling the problem on a Grassmann manifold.
Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames
This paper proposes a new approach for monocular dense 3D reconstruction of a complex dynamic scene from two perspective frames.
Spatial-Temporal Union of Subspaces for Multi-body Non-rigid Structure-from-Motion
This spatio-temporal representation not only provides competitive 3D reconstruction but also outputs robust segmentation of multiple non-rigid objects.
Multi-body Non-rigid Structure-from-Motion
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).
