Object Reconstruction
78 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Object Reconstruction
A Large Dataset of Object Scans
HOWS
Latest papers with no code
Reconstructing Hand-Held Objects in 3D
At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is small relative to all possible objects.
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion
Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion.
Towards 3D Vision with Low-Cost Single-Photon Cameras
We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras.
UPNeRF: A Unified Framework for Monocular 3D Object Reconstruction and Pose Estimation
Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose.
MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time.
NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction
Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation.
2L3: Lifting Imperfect Generated 2D Images into Accurate 3D
Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures.
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.
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes.
RHOBIN Challenge: Reconstruction of Human Object Interaction
Modeling the interaction between humans and objects has been an emerging research direction in recent years.
