[ECCV 2026] 3DZip: Spatial-Aware Feature Diversity-Guided Token Compression for 3D Question Answering

Changwoo Baek  ·  Kyeongbo Kong^†

Pusan National University

📰 News

• 2026-06-20 — 🚀 Code & project page released!
• 2026-06-18 — 🎉 3DZip is accepted to ECCV 2026!

🗺️ Release Plan

• 3DZip++ — extended version
• Paper
• Inference & evaluation code (LLaVA-3D)

✨ Highlight

https://github.com/user-attachments/assets/5776e4df-a16b-47c9-ad09-e0f909ecd2c8

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3DZip is a training-free, three-stage token compression framework for projection-based 3D vision-language models. It removes the redundancy introduced by multi-view aggregation while preserving geometric coherence, retaining 94.7% of the original performance with only 128 tokens and a 1.92× faster inference speed.

Recent 3D VLMs construct geometry-aware tokens by projecting 2D visual features into world coordinates, generating thousands of tokens per scene. Existing 2D token-compression methods rely on semantic relevance or attention, which overlook the structured spatial nature of 3D tokens, and object-level imbalance persists even after spatial aggregation. 3DZip first applies coarse voxelization to remove point-level redundancy, then selects anchor tokens by feature-space diversity via a Determinantal Point Process (DPP), and finally merges the remaining tokens under spatial constraints to preserve geometric structure.

🧠 3DZip algorithm

The core algorithm is implemented in llava/model/multimodal_encoder/video_encoder.py — see the 3dzip pooling branch. It consists of three stages:

1. Voxelization — group visual tokens by a 3D voxel grid using scatter_mean.
2. DPP selection — greedy MAP inference via Cholesky decomposition on a cosine-similarity kernel; selects the k most diverse anchor tokens.
3. Cosine merge — assign remaining voxels to the nearest anchor (cosine distance) and merge by uniform averaging with a spatial cutoff.

This code is built on top of LLaVA-3D. Please refer to the original repository for full documentation, training scripts, and model details.

🛠️ Setup

Prerequisites

ScanNet data access is required. Please sign the ScanNet Terms of Use and follow the instructions at ScanNet to obtain the data.

Environment

conda create -n 3dzip python=3.10 -y
conda activate 3dzip

pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118
pip install -e .

# torch-scatter (required for voxelization)
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.2+cu118.html

For the full environment setup, refer to the LLaVA-3D installation guide.

Data

The evaluation benchmarks (ScanQA, SQA3D) are 3D question-answering tasks based on ScanNet scenes. Scene data should be placed under playground/data/LLaVA-3D-Pretrain/scannet/, with each scene folder containing color/, depth/, pose/, and intrinsic/ subdirectories. Evaluation annotations should be placed under playground/data/annotations/, derived from SQA3D and ScanQA.

🚀 Usage

Environment variables

Variable	Description	Default
POOLING	Pooling method: voxelize (baseline) or 3dzip (ours)	voxelize
VOXEL_SIZE	Voxel grid size in meters	0.2
ADAPTIVE_RATIO	Target token count (e.g., 32, 64, 128). 0 = use fixed VOXEL_SIZE	0
MERGE_CUTOFF	Max voxel-grid distance for merge	5

Evaluation

Each eval script takes three arguments: <pooling> <voxel_size> <target_tokens>.

# Baseline (LLaVA-3D native)
CUDA_VISIBLE_DEVICES=0 bash scripts/eval/eval_sqa3d.sh native

# 3DZip with 32 / 64 / 128 target tokens
CUDA_VISIBLE_DEVICES=0 bash scripts/eval/eval_sqa3d.sh 3dzip 0.2 32
CUDA_VISIBLE_DEVICES=0 bash scripts/eval/eval_sqa3d.sh 3dzip 0.2 64
CUDA_VISIBLE_DEVICES=0 bash scripts/eval/eval_sqa3d.sh 3dzip 0.2 128

Available benchmarks:

CUDA_VISIBLE_DEVICES=0 bash scripts/eval/eval_sqa3d.sh   <pooling> <voxel_size> <target_tokens>   # SQA3D
CUDA_VISIBLE_DEVICES=0 bash scripts/eval/eval_scanqa.sh  <pooling> <voxel_size> <target_tokens>   # ScanQA

Per-scene feature cache (optional)

For embodied deployments where the same 3D scene is queried many times, pass --feature-cache to reuse the vision tower's output across queries on the same scene. Off by default; preserves model output.

📁 Repository structure

.
├── docs/                   # Project page (served at paper.pnu-cvsp.com/3DZip via GitHub Pages)
│   ├── index.html
│   └── static/             # Page assets (figures, teaser video, logo)
├── llava/                  # LLaVA-3D + 3DZip implementation
│   └── model/multimodal_encoder/video_encoder.py   # core 3DZip algorithm
├── playground/data/        # Evaluation annotations (ScanQA, SQA3D)
├── scripts/eval/           # Evaluation scripts
└── pyproject.toml

🙏 Acknowledgements

This code is built upon LLaVA-3D; we thank the authors for their excellent work. The project page uses the Academic Project Page Template, adapted from Nerfies.

3DZip is part of Awesome 3D Token Compression — a curated reading list of token pruning / merging methods for 3D multimodal LLMs.

📄 License

This project follows the license of LLaVA-3D, released under CC BY-NC-SA 4.0 (non-commercial). The underlying code inherits the Apache-2.0 license from LLaVA.