Media Summary: Video for the IROS 2023 submission. The video presents the experimental results of the paper " What if a 3D scene wasn't stored as geometry… but as a mathematical function? That's exactly what Authors: Zhongpai Gao (Shanghai Jiao Tong University )* Description:

Continuous Implicit Sdf Based Any - Detailed Analysis & Overview

Video for the IROS 2023 submission. The video presents the experimental results of the paper " What if a 3D scene wasn't stored as geometry… but as a mathematical function? That's exactly what Authors: Zhongpai Gao (Shanghai Jiao Tong University )* Description: Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent ... What if you could represent 3D shapes with perfect smoothness — at Reconstructing 3D vehicles from noisy and sparse partial point clouds is of great significance to autonomous driving. Most existing ...

Hello, everyone. In this video, I am going to explain this paper to you. DISN: Deep This video presents our research paper "Accelerating Signed Distance Functions", published and presented at Pacific Graphics ... For 3D reconstruction and representation, we train a neural model to predict an unsigned distance field as opposed to the more ...

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Continuous Implicit SDF Based Any-shape Robot Trajectory Optimization
Implicit Representations Explained | The Future of 3D AI
SIGGRAPH 2024 & TOG: Implicit Swept Volume SDF
Chen-Hsuan Lin: SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Learning Continuous Mesh Representation with Spherical Implicit Surface
PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices
IROS 2023
Signed Distance Functions (SDFs) Explained | Smooth 3D Representation
[ICCV 2023] MV-DeepSDF
Deep Implicit Surface Network| DISN| High-quality 3D Reconstruction| +91-8283824812
CSC2547   DeepSDF  Learning Continuous Signed Distance Functions for Shape Representation
Accelerating Signed Distance Functions — Pacific Graphics 2025
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Continuous Implicit SDF Based Any-shape Robot Trajectory Optimization

Continuous Implicit SDF Based Any-shape Robot Trajectory Optimization

Video for the IROS 2023 submission. The video presents the experimental results of the paper "

Implicit Representations Explained | The Future of 3D AI

Implicit Representations Explained | The Future of 3D AI

What if a 3D scene wasn't stored as geometry… but as a mathematical function? That's exactly what

SIGGRAPH 2024 & TOG: Implicit Swept Volume SDF

SIGGRAPH 2024 & TOG: Implicit Swept Volume SDF

Video for the SIGGRAPH 2024 & TOG:

Chen-Hsuan Lin: SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images

Chen-Hsuan Lin: SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images

PAPER LINKS:

Learning Continuous Mesh Representation with Spherical Implicit Surface

Learning Continuous Mesh Representation with Spherical Implicit Surface

Authors: Zhongpai Gao (Shanghai Jiao Tong University )* Description:

PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices

PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices

Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent ...

IROS 2023

IROS 2023

Continuous Implicit SDF Based Any

Signed Distance Functions (SDFs) Explained | Smooth 3D Representation

Signed Distance Functions (SDFs) Explained | Smooth 3D Representation

What if you could represent 3D shapes with perfect smoothness — at

[ICCV 2023] MV-DeepSDF

[ICCV 2023] MV-DeepSDF

Reconstructing 3D vehicles from noisy and sparse partial point clouds is of great significance to autonomous driving. Most existing ...

Deep Implicit Surface Network| DISN| High-quality 3D Reconstruction| +91-8283824812

Deep Implicit Surface Network| DISN| High-quality 3D Reconstruction| +91-8283824812

Hello, everyone. In this video, I am going to explain this paper to you. DISN: Deep

CSC2547   DeepSDF  Learning Continuous Signed Distance Functions for Shape Representation

CSC2547 DeepSDF Learning Continuous Signed Distance Functions for Shape Representation

Paper Title: DeepSDF: Learning

Accelerating Signed Distance Functions — Pacific Graphics 2025

Accelerating Signed Distance Functions — Pacific Graphics 2025

This video presents our research paper "Accelerating Signed Distance Functions", published and presented at Pacific Graphics ...

Neural Unsigned Distance Fields for Implicit Function Learning (NeurIPS 2020)

Neural Unsigned Distance Fields for Implicit Function Learning (NeurIPS 2020)

For 3D reconstruction and representation, we train a neural model to predict an unsigned distance field as opposed to the more ...