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Found 38 papers, 19 papers with code
Mixed Diffusion for 3D Indoor Scene Synthesis
Realistic conditional 3D scene synthesis significantly enhances and accelerates the creation of virtual environments, which can also provide extensive training data for computer vision and robotics research among other applications.
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Our OpenNeRF further leverages NeRF's ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images.
3D scene generation from scene graphs and self-attention
Synthesizing realistic and diverse indoor 3D scene layouts in a controllable fashion opens up applications in simulated navigation and virtual reality.
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS
First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization.
Ranked #1 on
Novel View Synthesis
on Mip-NeRF 360
KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly match the target.
Denoising Diffusion via Image-Based Rendering
In this work, we introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes.
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image.
SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
Leveraging the advantage of DINOv2 in providing SE(3)-consistent semantic features, we hierarchically extract two types of SE(3)-invariant geometric features to further encapsulate local-to-global object-specific information.
MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision
Previous works concerning single-view hand-held object reconstruction typically rely on supervision from 3D ground-truth models, which are hard to collect in real world.
SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs
In this paper, we present SG-Bot, a novel rearrangement framework that utilizes a coarse-to-fine scheme with a scene graph as the scene representation.
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction
However, such strategies fail to consistently align the denoised point cloud with the given image, leading to unstable conditioning and inferior performance.
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target.
View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection
For autonomous vehicles, driving safely is highly dependent on the capability to correctly perceive the environment in 3D space, hence the task of 3D object detection represents a fundamental aspect of perception.
TextMesh: Generation of Realistic 3D Meshes From Text Prompts
In addition, we propose a novel way to finetune the mesh texture, removing the effect of high saturation and improving the details of the output 3D mesh.
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction
Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems.
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images.
SPARF: Neural Radiance Fields from Sparse and Noisy Poses
Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views.
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box.
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
6-DoF robotic grasping is a long-lasting but unsolved problem.
SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation
In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network.
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories.
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental yet challenging problem in computer vision.
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.
Ranked #1 on
6D Pose Estimation
on LineMOD
(Mean ADD-S metric)
Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection
Monocular 3D object detection continues to attract attention due to the cost benefits and wider availability of RGB cameras.
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
We first present a small sequence of RGB-D images displaying a human-object interaction.
Object-aware Monocular Depth Prediction with Instance Convolutions
With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography.
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs
Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content.
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.
Ranked #1 on
6D Pose Estimation using RGB
on Occlusion LineMOD
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations.
Ranked #3 on
6D Pose Estimation using RGB
on Occlusion LineMOD
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental problem in computer vision.
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances.
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.
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.
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
