Search Results for author:
Found 9 papers, 2 papers with code
DCUDF2: Improving Efficiency and Accuracy in Extracting Zero Level Sets from Unsigned Distance Fields
Unsigned distance fields (UDFs) allow for the representation of models with complex topologies, but extracting accurate zero level sets from these fields poses significant challenges, particularly in preserving topological accuracy and capturing fine geometric details.
A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions
Unsigned distance fields (UDFs) provide a versatile framework for representing a diverse array of 3D shapes, encompassing both watertight and non-watertight geometries.
GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting
The initial SDF represents the coarse geometry of the target object.
Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction
While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures.
Multi-View Attentive Contextualization for Multi-View 3D Object Detection
We present Multi-View Attentive Contextualization (MvACon), a simple yet effective method for improving 2D-to-3D feature lifting in query-based multi-view 3D (MV3D) object detection.
Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior
Existing neural rendering-based text-to-3D-portrait generation methods typically make use of human geometry prior and diffusion models to obtain guidance.
Practical Measurements of Translucent Materials with Inter-Pixel Translucency Prior
Material appearance is a key component of photorealism with a pronounced impact on human perception.
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning.
BERT-JAM: Boosting BERT-Enhanced Neural Machine Translation with Joint Attention
In this work, we propose a novel BERT-enhanced NMT model called BERT-JAM which improves upon existing models from two aspects: 1) BERT-JAM uses joint-attention modules to allow the encoder/decoder layers to dynamically allocate attention between different representations, and 2) BERT-JAM allows the encoder/decoder layers to make use of BERT's intermediate representations by composing them using a gated linear unit (GLU).
