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Found 19 papers, 10 papers with code
Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e. g., the Janus face problem or the content drift problem, in zero-shot text-to-3D models.
Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
Specifically, we decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions.
MVDream: Multi-view Diffusion for 3D Generation
We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt.
ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
Unlike traditional multi-view stereo approaches, the neural implicit surface-based methods leverage neural networks to represent 3D scenes as signed distance functions (SDFs).
Physically Plausible 3D Human-Scene Reconstruction from Monocular RGB Image using an Adversarial Learning Approach
This paper proposes using an implicit feature representation of the scene elements to distinguish a physically plausible alignment of humans and objects from an implausible one.
Contextualising Implicit Representations for Semantic Tasks
Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks.
Neural Refinement for Absolute Pose Regression with Feature Synthesis
To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy.
MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices
The distinct data modality offered by high-resolution RGB images and low-resolution depth maps captured on a mobile device, when combined with precise 3D geometry annotations, presents a unique opportunity for future research on high-fidelity 3D reconstruction.
NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior
Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes.
Object-Compositional Neural Implicit Surfaces
This paper proposes a novel framework, ObjectSDF, to build an object-compositional neural implicit representation with high fidelity in 3D reconstruction and object representation.
BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion
Dense 3D reconstruction from a stream of depth images is the key to many mixed reality and robotic applications.
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.
Ray-ONet: Efficient 3D Reconstruction From A Single RGB Image
We propose Ray-ONet to reconstruct detailed 3D models from monocular images efficiently.
MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos
Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localise objects of interest and extract their shape codes that represent the object shapes in a learned embedding space.
FroDO: From Detections to 3D Objects
We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner.
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette
3D shape reconstruction from a single image is a highly ill-posed problem.
Real-Time Monocular Object-Model Aware Sparse SLAM
While sparse point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information.
Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields
Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object.
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
