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Zero-Shot Multi-Object Scene Completion (ECCV 2024)

OctMAE

Zero-Shot Multi-Object Scene Completion
Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rareș Ambruș, Sergey Zakharov
Paper, Project Page

Abstract

We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Despite notable advancements in single-object 3D shape completion, high-quality reconstructions in highly cluttered real-world multi-object scenes remains a challenge. To address this issue, we propose OctMAE, an architecture that leverages an Octree U-Net and a latent 3D MAE to achieve high-quality and near real-time multi-object scene completion through both local and global geometric reasoning. Because a naive 3D MAE can be computationally intractable and memory intensive even in the latent space, we introduce a novel occlusion masking strategy and adopt 3D rotary embeddings, which significantly improves the runtime and scene completion quality. To generalize to a wide range of objects in diverse scenes, we create a large-scale photorealistic dataset, featuring a diverse set of 12K 3D object models from the Objaverse dataset which are rendered in multi-object scenes with physics-based positioning. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets and demonstrates a strong zero-shot capability.

News

  • 2024-07-01: OctMAE is accepted by ECCV 2024

Prerequisites

Environment Setup

Build a docker image

$ git clone --recursive [email protected]:TRI-ML/OctMAE.git
$ cd OctMAE
$ ./docker/build.sh

Run a docker image

$ ./docker/run.sh

Dataset Preparation

Note that our training and evaluation datasets have 20TB and 22GB of data respectively and is assumed to be stored in an S3 bucket. Since Webdataset is used for training and evaluation, you need to download, unzip, and re-upload the shard files to your own S3 bucket. Do not forget to replace {your_s3_path} in configs/default.yaml and eval.py with your S3 path. You can also download a tiny version of the training dataset (the first 10 shards of the training dataset) with train_tiny for visualization purposes.

# Download, unzip, and re-upload the shard files.
$ ./scripts/download.sh {your_data_path} {train_tiny|train|eval}
$ ./scripts/upload.sh {your_data_path} {train|eval} {your_s3_path}

Quickstart

Training

# From scratch
$ ./scripts/train.sh {your_wandb_project_name} {your_wandb_run_name} ./configs/default.yaml

# From a checkpoint
$ ./scripts/train.sh {your_wandb_project_name} {your_wandb_run_name} ./configs/default.yaml {path_to_checkpoint_file}

Evaluation

  • Download a pre-trained checkpoint
$ mkdir checkpoints
$ wget https://s3.amazonaws.com/tri-ml-public.s3.amazonaws.com/github/octmae/octmae.ckpt -P checkpoints/
  • Run evaluation
$ ./scripts/eval.sh ./configs/default.yaml checkpoints/octmae.ckpt {eval_dataset_name (synth_eval, ycb_video, hb, hope)}

Visualizations

COMING SOON!

Demo

COMING SOON!

Citation

@InProceedings{Iwase_ECCV_2024,
  author = {Iwase, Shun and, Liu, Katherine and Guizilini, Vitor and Gaidon, Adrien and Kitani, Kris and Ambruș, Rareș and Zakharov, Sergey},
  title = {Zero-Shot Multi-Object Scene Completion},
  booktitle = {ECCV},
  year = {2024}
}

License

This repository is released under the CC BY-NC 4.0 license.