Search Results for author: Selim Engin

Found 8 papers, 2 papers with code

FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection

no code implementations14 Dec 2023 Hongsuk Choi, Isaac Kasahara, Selim Engin, Moritz Graule, Nikhil Chavan-Dafle, Volkan Isler

While ControlNet provides control over the geometric form of the instances in the generated image, it lacks the capability to dictate the visual appearance of each instance.

Image Generation

VioLA: Aligning Videos to 2D LiDAR Scans

no code implementations8 Nov 2023 Jun-Jee Chao, Selim Engin, Nikhil Chavan-Dafle, Bhoram Lee, Volkan Isler

We study the problem of aligning a video that captures a local portion of an environment to the 2D LiDAR scan of the entire environment.

Depth Completion Image Inpainting

RIC: Rotate-Inpaint-Complete for Generalizable Scene Reconstruction

no code implementations21 Jul 2023 Isaac Kasahara, Shubham Agrawal, Selim Engin, Nikhil Chavan-Dafle, Shuran Song, Volkan Isler

General scene reconstruction refers to the task of estimating the full 3D geometry and texture of a scene containing previously unseen objects.

Autonomous Navigation

Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction

1 code implementation16 May 2023 Shubham Agrawal, Nikhil Chavan-Dafle, Isaac Kasahara, Selim Engin, Jinwook Huh, Volkan Isler

In this paper, we present a novel method to provide this geometric and semantic information of all objects in the scene as well as feasible grasps on those objects simultaneously.

3D Shape Reconstruction Object +1

Category-Level Global Camera Pose Estimation with Multi-Hypothesis Point Cloud Correspondences

no code implementations28 Sep 2022 Jun-Jee Chao, Selim Engin, Nicolai Häni, Volkan Isler

This paper proposes an optimization method that retains all possible correspondences for each keypoint when matching a partial point cloud to a complete point cloud.

Point Cloud Registration Pose Estimation

Higher Order Function Networks for View Planning and Multi-View Reconstruction

no code implementations4 Oct 2019 Selim Engin, Eric Mitchell, Daewon Lee, Volkan Isler, Daniel D. Lee

In contrast to offline methods which require a 3D model of the object as input or online methods which rely on only local measurements, our method uses a neural network which encodes shape information for a large number of objects.

3D Reconstruction Object

Higher-Order Function Networks for Learning Composable 3D Object Representations

no code implementations ICLR 2020 Eric Mitchell, Selim Engin, Volkan Isler, Daniel D. Lee

We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network.

Motion Planning Object

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