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Found 23 papers, 11 papers with code
TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes
However, the exploration of 3D dense captioning in outdoor scenes is hindered by two major challenges: 1) the \textbf{domain gap} between indoor and outdoor scenes, such as dynamics and sparse visual inputs, makes it difficult to directly adapt existing indoor methods; 2) the \textbf{lack of data} with comprehensive box-caption pair annotations specifically tailored for outdoor scenes.
LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment
Language-guided scene-aware human motion generation has great significance for entertainment and robotics.
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes, e. g., depth and normals, from single images.
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models.
MonoOcc: Digging into Monocular Semantic Occupancy Prediction
However, existing methods rely on a complex cascaded framework with relatively limited information to restore 3D scenes, including a dependency on supervision solely on the whole network's output, single-frame input, and the utilization of a small backbone.
GaussianPro: 3D Gaussian Splatting with Progressive Propagation
The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed.
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context.
Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization.
GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces
In this paper, we present GaussianShader, a novel method that applies a simplified shading function on 3D Gaussians to enhance the neural rendering in scenes with reflective surfaces while preserving the training and rendering efficiency.
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions.
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities.
Wonder3D: Single Image to 3D using Cross-Domain Diffusion
In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry.
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image.
NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images
We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment.
NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing
In this paper, we propose to leverage implicit Signed Distance Function (SDF) as surface representation, and optimize the SDF field via volume rendering with a self-occlusion aware refractive ray tracing.
Learning Long-Range Information with Dual-Scale Transformers for Indoor Scene Completion
Due to the limited resolution of 3D sensors and the inevitable mutual occlusion between objects, 3D scans of real scenes are commonly incomplete.
NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors
The key idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in a neural rendering framework for reconstructing large texture-less shapes and, importantly, to do this in an adaptive manner to also enable the reconstruction of irregular shapes with fine details.
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images.
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
Adaptive Surface Normal Constraint for Depth Estimation
We present a novel method for single image depth estimation using surface normal constraints.
Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks
We present a novel method for multi-view depth estimation from a single video, which is a critical task in various applications, such as perception, reconstruction and robot navigation.
Occlusion-Aware Depth Estimation with Adaptive Normal Constraints
We present a new learning-based method for multi-frame depth estimation from a color video, which is a fundamental problem in scene understanding, robot navigation or handheld 3D reconstruction.
