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LICENSE

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NOTICE

@@ -0,0 +1,262 @@
+ShapeR - Third-Party Notices and Attributions
+==============================================
+
+This project includes code from several third-party sources, each with their
+own licensing terms as detailed below.
+
+==============================================================================
+1. DINOv2 (model/dinov2/)
+==============================================================================
+
+The model/dinov2 directory and the dino checkpoints are from Meta AI's DINOv2 project.
+
+Source: https://github.com/facebookresearch/dinov2
+License: Apache License 2.0
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+==============================================================================
+2. Flow Matching Library (model/flow_matching/helpers/)
+==============================================================================
+
+The model/flow_matching/helpers directory contains code derived from Meta AI's
+Flow Matching library.
+
+Source: https://github.com/facebookresearch/flow_matching
+License: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+This code is licensed under CC BY-NC 4.0. See:
+http://creativecommons.org/licenses/by-nc/4.0/
+
+==============================================================================
+3. Flux Dual Stream Transformer (model/flow_matching/dualstream_transformer.py)
+==============================================================================
+
+The dualstream_transformer.py file is inspired by the Flux model's dual stream
+transformer architecture.
+
+Source: https://github.com/black-forest-labs/flux
+License: Apache License 2.0
+
+Copyright (c) Black Forest Labs.
+
+Licensed under the Apache License, Version 2.0.
+
+==============================================================================
+4. Flux Text Embedder (model/text/hf_embedder.py)
+==============================================================================
+
+The hf_embedder.py file is derived from the Flux conditioner module.
+
+Source: https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/conditioner.py
+License: Apache License 2.0
+
+Copyright (c) Black Forest Labs.
+
+Licensed under the Apache License, Version 2.0.
+
+==============================================================================
+5. DORA-VAE 3D Autoencoder (model/vae3d/)
+==============================================================================
+
+The model/vae3d directory contains code inspired by the DORA-VAE project.
+
+Source: https://github.com/Seed3D/Dora
+License: Apache License 2.0
+
+Licensed under the Apache License, Version 2.0.
+
+==============================================================================
+Apache License 2.0 Full Text
+==============================================================================
+
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+   END OF TERMS AND CONDITIONS

README.md

@@ -1,3 +1,89 @@
+# ShapeR
+
+![teaser](resources/Teaser.jpg)
+
+ShapeR introduces a novel approach to metric shape generation. Given an input image sequence, preprocessing extracts per-object metric sparse SLAM points, images, poses, and captions using off-the-shelf methods. A rectified flow transformer operating on VecSet latents conditions on these multimodal inputs to generate a shape code, which is decoded into the object’s mesh. By
+applying the model object-centrically to each detected object, we obtain a metric reconstruction of the entire scene.
+
+[Repository](https://github.com/facebookresearch/ShapeR) | [Paper]()
+
+## Usage
+
+Clone the [repository](https://github.com/facebookresearch/ShapeR) and follow the INSTALL.md instructions to install the required dependencies.
+
+### Inference
+
+```bash
+python infer_shape.py --input_pkl <sample.pkl> --config balance --output_dir output
+```
+
+**Arguments:**
+- `--input_pkl`: Path to preprocessed pickle file (relative to `data/`)
+- `--config`: Preset configuration
+  - `quality`: 16 views, 50 steps (best quality, slowest)
+  - `balance`: 16 views, 25 steps (recommended)
+  - `speed`: 4 views, 10 steps (fastest)
+- `--output_dir`: Output directory for meshes and visualizations
+- `--do_transform_to_world`: Transform output mesh to world coordinates
+- `--remove_floating_geometry`: Remove disconnected mesh components (default: on)
+- `--simplify_mesh`: Reduce mesh complexity (default: on)
+- `--save_visualization`: Save comparison visualization (default: on)
+
+### Example
+
+```bash
+python infer_shape.py --input_pkl gaia_29093018117008733__20250415_125853.pkl --config balance
+```
+
+**Output:**
+- `<name>.glb`: Reconstructed 3D mesh
+- `VIS__<name>.jpg`: Visualization comparing input, prediction, and ground truth
+
+![Example visualization](resources/output_vis_example.jpg)
+
+## Data Format
+
+This codebase assumes that A sequence has already been processed using the Aria MPS pipeline, along with an 3D object instance detector, resulting in the pickle files with the preprocessed data required for ShapeR ingestion.
+
+We release the [**ShapeR Evaluation Dataset**](https://huggingface.co/datasets/facebook/ShapeR-Evaluation) containing preprocessed samples from Aria glasses captures. Each sample is a pickle file with point clouds, multi-view images, camera parameters, text captions, and ground truth meshes.
+
+For a detailed walkthrough of the data format, see the **[`explore_data.ipynb`](https://github.com/facebookresearch/ShapeR/blob/main/explore_data.ipynb)** notebook which includes:
+- Complete pickle file structure with all keys and their dimensions
+- Interactive 3D visualization of point clouds and meshes
+- Camera position visualization
+- Image and mask grid displays
+- DataLoader usage examples for both SLAM and RGB variants
+- Explanation of view selection strategies
+
+## Project Structure
+
+```
+ShapeR/
+├── infer_shape.py          # Main inference script
+├── explore_data.ipynb      # Data exploration notebook
+├── dataset/
+│   ├── shaper_dataset.py   # Dataset and dataloader
+│   ├── image_processor.py  # View selection and image preprocessing
+│   └── point_cloud.py      # Point cloud to SparseTensor
+├── model/
+│   ├── flow_matching/
+│   │   └── shaper_denoiser.py  # Flow matching denoiser
+│   ├── vae3d/
+│   │   └── autoencoder.py      # 3D VAE for mesh generation
+│   ├── pointcloud_encoder.py   # Sparse 3D convolution encoder
+│   └── dino_and_ray_feature_extractor.py  # Image feature extraction
+├── preprocessing/
+│   └── helper.py           # Fisheye rectification, camera utils
+├── postprocessing/
+│   └── helper.py           # Mesh cleanup, visualization
+├── checkpoints/            # Model weights (downloaded automatically)
+└── data/                   # Input pickle files
+```
+
+## License
+
+The ShapeR model are licensed under CC-BY-NC. See the [LICENSE](LICENSE) file for details. However portions of the project use checkpoints under separate license terms: see [NOTICE](NOTICE). e.g. DinoV2 is licensed under Apache-2.0 license.
+
 ---
 license: cc-by-nc-4.0
 language:
@@ -6,4 +92,4 @@ tags:
 - 3d-reconstruction
 - 3d-generation
 - generative-ai
----
+---

config.yaml

@@ -0,0 +1,34 @@
+encoder:
+  type: point_image_text
+  x0_mode: random
+  num_bins: 256
+  input_channels: 3
+  d_model: 768
+  conv_layers:
+  - 32
+  - 64
+  - 128
+  - 256
+  dino_image_size: 280
+  dino_mask_inject: true
+  dino_rot90inputs: true
+  dino_use_giant_model: true
+  dino_legacy_upsample: false
+  use_pre_text_attn_blocks: true
+  use_sam2_features: false
+fm_transformer:
+  hidden_size: 1024
+  num_heads: 16
+  mlp_ratio: 2.0
+  qkv_bias: true
+  depth: 16
+  depth_single_blocks: 32
+  time_sampler: flux
+vae:
+  embed_dim: 64
+dataset:
+  load_image_mode: composite
+  num_views: 2
+  variable_num_views: false
+  semi_dense_threshold_theta: 0.002
+  semi_dense_threshold_phi: 0.01

dinov2_vitl14_reg4_pretrain.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36e4deffbaef061a2576705b0c36f93621e2ae20bf6274694821b0b492551b51
+size 1217607321

vae-088-0-bfloat16.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd55f48bf291e3f783ef1a17c96331ba5dc81f93422f4e98008c5315e05bb196
+size 383306451

vae-config.yaml

@@ -0,0 +1,30 @@
+vae:
+  use_downsample: true
+  num_latents: 256
+  point_feats: 3
+  out_dim: 1
+  embed_dim: 64
+  width: 768
+  heads: 12
+  num_encoder_layers: 8
+  num_decoder_layers: 16
+  init_scale: 0.25
+  qkv_bias: false
+  use_ln_post: true
+  use_udf_extraction: true
+  token_scales:
+  - 128.0
+  - 256.0
+  - 384.0
+  - 512.0
+  - 640.0
+  - 1024.0
+  - 2048.0
+  token_probability:
+  - 0.025
+  - 0.025
+  - 0.025
+  - 0.025
+  - 0.05
+  - 0.2
+  - 0.65