j96w/DenseFusion

Health v2 · maintenance only

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1276 stars · 308 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DenseFusion is the official PyTorch implementation of the paper '6D Object Pose Estimation by Iterative Dense Fusion', which estimates the 6D pose of known objects from RGB-D images using a dense pixel-wise feature fusion network with iterative refinement. It includes training and evaluation code for the YCB-Video and LineMOD datasets plus trained checkpoints.

Use cases

• estimate 6D pose of objects from RGB-D images
• train a pose estimation model on YCB-Video dataset
• evaluate pose estimation on LineMOD
• run pose estimation for robot grasping research
• reproduce DenseFusion paper results
• fine-tune pose estimation on my own dataset

When to choose

• you need RGB-D based 6D object pose estimation for robotics or research
• you want to reproduce or build on the DenseFusion paper
• you need trained checkpoints for YCB-Video or LineMOD benchmarks

When to avoid

• you need actively maintained code with modern PyTorch versions
• you need CPU-only training, which is extremely slow
• you need a production-ready pose estimation pipeline with segmentation included
• you need Python 3.10+ or recent CUDA support out of the box

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision robotics deep-learning machine-learning python pose-estimation rgbd pytorch 6d-pose research-code object-pose linemod ycb-video linux gpu cuda

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