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Found 18 papers, 7 papers with code
GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline.
Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset
Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
DPHMs: Diffusion Parametric Head Models for Depth-based Tracking
We introduce Diffusion Parametric Head Models (DPHMs), a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences.
3D Scene Diffusion Guidance using Scene Graphs
Guided synthesis of high-quality 3D scenes is a challenging task.
NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud
We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin.
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis
We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes.
Learning 3D Scene Priors with 2D Supervision
Holistic 3D scene understanding entails estimation of both layout configuration and object geometry in a 3D environment.
PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories
While 3D shape representations enable powerful reasoning in many visual and perception applications, learning 3D shape priors tends to be constrained to the specific categories trained on, leading to an inefficient learning process, particularly for general applications with unseen categories.
Pose2Room: Understanding 3D Scenes from Human Activities
To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment.
ME-PCN: Point Completion Conditioned on Mask Emptiness
Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.
Surgical Instruction Generation with Transformers
Automatic surgical instruction generation is a prerequisite towards intra-operative context-aware surgical assistance.
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
Symmetric Dilated Convolution for Surgical Gesture Recognition
The experiment results demonstrate the ability of our method on capturing long-term frame dependencies, which largely outperform the state-of-the-art methods on the frame-wise accuracy up to ~6 points and the F1@50 score ~6 points.
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction.
Ranked #2 on
3D Shape Reconstruction
on Pix3D
Shallow2Deep: Indoor Scene Modeling by Single Image Understanding
Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling.
