# SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization

arXiv: https://arxiv.org/abs/2307.07356 · HTML: https://mlsanity.com/paper/2307.07356/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2023.

## Built on by (most cited first, 6 of 6)

- [Deliberate Planning of 3D Bin Packing on Packing Configuration Trees](https://mlsanity.com/paper/2504.04421/) (2025)
- [Transport of Algebraic Structure to Latent Embeddings](https://mlsanity.com/paper/2405.16763/) (2024)
- [GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention](https://mlsanity.com/paper/2406.07579/) (2024)
- [Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations](https://mlsanity.com/paper/2312.06791/) (2023)
- [HERB: Human-augmented Efficient Reinforcement learning for Bin-packing](https://mlsanity.com/paper/2504.16595/) (2025)
- [OPA-Pack: Object-Property-Aware Robotic Bin Packing](https://mlsanity.com/paper/2505.13339/) (2025)

## Abstract (arXiv)

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings.