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Found 18 papers, 9 papers with code
SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography
3D Gaussian Splatting (3DGS) has emerged as a premier method for 3D representation due to its real-time rendering and high-quality outputs, underscoring the critical need to protect the privacy of 3D assets.
RelayGS: Reconstructing Dynamic Scenes with Large-Scale and Complex Motions via Relay Gaussians
Finally, we jointly learn the scene's temporal motion and refine the canonical Gaussians learned from the first two stages.
InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception
3D scene understanding has become an essential area of research with applications in autonomous driving, robotics, and augmented reality.
MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines
We further present error analysis to unveil current LMMs still struggle to fully grasp the multimodal search tasks, and conduct ablation study to indicate the potential of scaling test-time computation for AI search engine.
OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding
To ensure robust feature presentation and 3D point-level understanding, we first employ SAM masks without cross-frame associations to train instance features with 3D consistency.
Hybrid Fourier Score Distillation for Efficient One Image to 3D Object Generation
We note that there is a disparity between the generation priors of these two diffusion models, leading to their different appearance outputs.
S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM
With the emergence of Neural Radiance Fields (NeRF), neural implicit representations have gained widespread applications across various domains, including simultaneous localization and mapping.
Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has significantly advanced 3D scene reconstruction and novel view synthesis.
Language-Assisted 3D Scene Understanding
The scale and quality of point cloud datasets constrain the advancement of point cloud learning.
GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
Our method achieves state-of-the-art performance in both relighting and novel view synthesis tasks among the recently proposed inverse rendering methods while achieving real-time rendering.
UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling
Subsequently, in the part of object state estimation, we propose a tightly coupled optimization model for object pose and scale estimation, incorporating hybrids constraints into a novel dual sliding window optimization framework for joint estimation.
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching.
Implicit Neural Representation for Cooperative Low-light Image Enhancement
Finally, extensive experiments demonstrate the robustness and superior effectiveness of our proposed NeRCo.
BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap
Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes.
Panoptic Compositional Feature Field for Editable Scene Rendering With Network-Inferred Labels via Metric Learning
In this work, we attempt to learn an object-compositional neural implicit representation for editable scene rendering by leveraging labels inferred from the off-the-shelf 2D panoptic segmentation networks instead of the ground truth annotations.
EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding
3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues.
Object SLAM-Based Active Mapping and Robotic Grasping
The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping.
EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms.
