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Found 15 papers, 6 papers with code
MVD-HuGaS: Human Gaussians from a Single Image via 3D Human Multi-view Diffusion Prior
We first generate multi-view images from the single reference image with an enhanced multi-view diffusion model, which is well fine-tuned on high-quality 3D human datasets to incorporate 3D geometry priors and human structure priors.
LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis
Then we propose a suitable solution according to the modality differences of image, audio, and video generation.
GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering
Recovering the intrinsic physical attributes of a scene from images, generally termed as the inverse rendering problem, has been a central and challenging task in computer vision and computer graphics.
GS-ID: Illumination Decomposition on Gaussian Splatting via Diffusion Prior and Parametric Light Source Optimization
We present GS-ID, a novel framework for illumination decomposition on Gaussian Splatting, achieving photorealistic novel view synthesis and intuitive light editing.
Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels.
Advances in 3D Generation: A Survey
In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications.
Sur2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images
Technically, we learn two parallel streams of an implicit signed distance field and an explicit surrogate surface Sur2f mesh, and unify volume rendering of the implicit signed distance function (SDF) and surface rendering of the surrogate mesh with a shared, neural shader; the unified shading promotes their convergence to the same, underlying surface.
GS-IR: 3D Gaussian Splatting for Inverse Rendering
We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results.
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization
Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research.
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention
These multi-view methods either refine the proposals predicted from single view via fused features, or fuse the features without considering the global spatial context; their performance is limited consequently.
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks
State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminative at semantic and instance levels, followed by a separate step of point grouping for proposing object instances.
Ranked #10 on
3D Instance Segmentation
on S3DIS
TAG : Type Auxiliary Guiding for Code Comment Generation
Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e. g., operator, string, etc.
FGN: Fully Guided Network for Few-Shot Instance Segmentation
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training.
An Encoder-Decoder Framework Translating Natural Language to Database Queries
These techniques help the neural network better focus on understanding semantics of operations in natural language and save the efforts on SQL grammar learning.
