Search Results for author:
Found 14 papers, 4 papers with code
Photo-realistic Neural Domain Randomization
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).
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection
3D object detection from monocular images is an ill-posed problem due to the projective entanglement of depth and scale.
3D Object Detection
3D Object Detection From Monocular Images
+4
Differentiable Rendering: A Survey
Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation.
Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data.
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core
We present a novel method to track 3D models in color and depth data.
3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin
In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks.
DeceptionNet: Network-Driven Domain Randomization
We present a novel approach to tackle domain adaptation between synthetic and real data.
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
We present a deep learning method for end-to-end monocular 3D object detection and metric shape retrieval.
Deep Model-Based 6D Pose Refinement in RGB
We present a novel approach for model-based 6D pose refinement in color data.
BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot.
Ranked #1 on
6D Pose Estimation using RGBD
on Tejani
An Octree-Based Approach towards Efficient Variational Range Data Fusion
Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime.
Hashmod: A Hashing Method for Scalable 3D Object Detection
We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data.
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting.
