High-Resolution Image Synthesis with Latent Diffusion Models

Adaptive Latent Diffusion Model for 3D Medical Image

T2I-Adapter

Resources of the paper “Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models”.

Code release for GECCO: Geometrically-Conditioned Point Diffusion Models

Code release for GECCO: Geometrically-Conditioned Point Diffusion Models

Slicer extension for automated segmentation of individual teeth in cone-beam CT dental scans using a deep-learning based approach.

[CVPR2022] Geometric Transformer for Fast and Robust Point Cloud Registration

Let your Claude able to think

[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"

[IEEE J-BHI] An Arbitrary Scale Super-Resolution Approach for 3D MR Images using Implicit Neural Representation

Controllable Point Cloud and Mesh Generation

PyTorch implementation of RandLA-Net

PyTorch implementation of RandLA-Net

[ CVPR 2025 ] We introduce LT3SD, a novel latent 3D scene diffusion approach enabling high-fidelity generation of infinite 3D environments in a patch-by-patch and coarse-to-fine fashion.

pytorch structural similarity (SSIM) loss

Official code for "Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction" (CVPR 2023)

(ACM MM ASIA 2021) Patch-Based Deep Autoencoder for Point Cloud Geometry Compression.

[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning

[CVPR'23] Official PyTorch implementation of 3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process

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[ECCV 2024] Occupancy as Set of Points