Lingbot-map: A 3D foundation model for reconstructing scenes from streaming data

teaser.mp4 #

🗺️ Meet LingBot-Map! We've built a feed-forward 3D foundation model for streaming 3D reconstruction! 🏗️🌍

LingBot-Map has focused on:

Geometric Context Transformer: Architecturally unifies coordinate grounding, dense geometric cues, and long-range drift correction within a single streaming framework through anchor context, pose-reference window, and trajectory memory.High-Efficiency Streaming Inference: A feed-forward architecture with paged KV cache attention, enabling stable inference at ~20 FPS on 518×378 resolution over long sequences exceeding 10,000 frames.State-of-the-Art Reconstruction: Superior performance on diverse benchmarks compared to both existing streaming and iterative optimization-based approaches.

Click to expand #

2026-06-28— Fixed an SDPA KV cache bug.** The SDPA backend now performs better on long sequences**. We still recommend the FlashInfer backend for the best performance.** 2026-05-25**— 📊** Evaluation benchmark released**. We released the evaluation scripts for KITTI and Oxford Spires — seebenchmark/for the pipeline, and runto prepare Oxford Spires data before evaluation.preprocess/oxford.py

2026-04-29— 📹** Long-video demo released**. We released a very-long-video example (~25 000 frames, 13-minute indoor walkthrough) rendered with the offline pipeline — seeWorked Examplefor the command, flag rationale, and rendered output.2026-04-27— 🚀** LingBot-Map accelerated**. Pull the latestmain

and runpython demo.py --compile ...

orpython gct_profile.py --backend flashinfer --dtype bf16 --compile

to verify on your hardware.2026-04-24— Fixed a FlashInfer KV cache bug where--keyframe_interval > 1

silently cached non-keyframes.You should now see better pose and reconstruction quality when running with more than 320 frames.

• ✅ Release evaluation benchmark

• ✅ Oxford Spires dataset

• ✅ KITTI dataset

• ✅ VBR dataset

• ✅ Droid-W dataset

• ✅ TUM-D dataset

• ✅ 7-scenes dataset

• ✅ ETH3D dataset

• ✅ Tanks and Temples dataset

• ✅ NRGBD dataset

• ✅ Release demo scripts

• ✅ Indoor long-video demo ( Featured indoor walkthrough) - ✅ Outdoor long-video demo

• ✅ LingBot-World demo ( Worked example) - ✅ Aerial long-video demo

• ✅ Indoor long-video demo (

1. Create conda environment

conda create -n lingbot-map python=3.10 -y
conda activate lingbot-map

2. Install PyTorch (CUDA 12.8)

pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128

PyTorch 2.8.0 is the recommended version because NVIDIA Kaolin (required by the batch rendering pipeline) has prebuilt wheels for

torch-2.8.0_cu128

. If you only needdemo.py

you may use a newer PyTorch, but the batch renderer then requires building Kaolin from source. For other CUDA versions, see[PyTorch Get Started].

3. Install lingbot-map

pip install -e .

4. Install FlashInfer (recommended)

FlashInfer provides paged KV cache attention for efficient streaming inference. It is a pure-Python package that JIT-compiles CUDA kernels on first use, so a single wheel works across CUDA/PyTorch versions:

pip install --index-url https://pypi.org/simple flashinfer-python

--index-url https://pypi.org/simple

is only needed if your default pip index is an internal mirror that doesn't haveflashinfer-python

. (Optional) For faster first-use, you can additionally install a CUDA-specific JIT cache:pip install flashinfer-jit-cache -f https://flashinfer.ai/whl/cu128/flashinfer-jit-cache/

. See[FlashInfer installation]for details. If FlashInfer is not installed, the model falls back to SDPA (PyTorch native attention) via--use_sdpa

.

5. Visualization dependencies (optional)

pip install -e ".[vis]"

Model Name	Huggingface Repository	ModelScope Repository	Description
lingbot-map-long

Robbyant/lingbot-maprobbyant/lingbot-mapRobbyant/lingbot-maprobbyant/lingbot-mapRobbyant/lingbot-map🚧

Coming soon:we're training an stronger model that supports longer sequences — stay tuned.

After installation, run your first scene with one command:

python demo.py --model_path /path/to/lingbot-map-long.pt \
    --image_folder example/courthouse --mask_sky

This launches an interactive viser viewer at http://localhost:8080

. See Interactive Demo below for the full set of scenes and flags, or jump to Offline Rendering Pipeline for long-sequence batch rendering.

Run demo.py

for interactive 3D visualization via a browser-based viser viewer (default http://localhost:8080

).

We provide four example scenes in example/

that you can run out of the box:

python demo.py --model_path /path/to/lingbot-map-long.pt \
    --image_folder example/courthouse --mask_sky

output_pointcloud_side_by_side.mp4 #

python demo.py --model_path /path/to/lingbot-map-long.pt \
    --image_folder example/university --mask_sky

output_pointcloud_side_by_side.mp4 #

python demo.py --model_path /path/to/lingbot-map-long.pt \
    --image_folder example/loop

output_pointcloud_side_by_side.mp4 #

python demo.py --model_path /path/to/lingbot-map-long.pt \
    --image_folder example/oxford --mask_sky

output_pointcloud_side_by_side.mp4 #

Sequence is too long for the interactive viser viewer — this clip was rendered with the Offline Rendering Pipeline. See that section for the full command.

We will provide more examples in the follow-up.

Use --keyframe_interval

to reduce KV cache memory by only keeping every N-th frame as a keyframe. Non-keyframe frames still produce predictions but are not stored in the cache. This is useful for long sequences which exceed 320 frames (We train with video RoPE on 320 views, so performance degrades when the KV cache stores more than 320 views. Using a keyframe strategy allows inference over longer sequences.).

Dataset: Download the demo sequences from robbyant/lingbot-map-demo on Hugging Face.

Example run on the travel

sequence from the dataset above (sky masking on, 4 camera optimization iterations, keyframe every 2 frames):

python demo.py \
    --image_folder /path/to/lingbot-map-demo/travel/ \
    --model_path /path/to/lingbot-map-long.pt \
    --mask_sky \
    --camera_num_iterations 4 \
    --keyframe_interval 2

output_pointcloud_side_by_side.mp4 #

Note on inference range.Our method does not perform state resetting by default, so the maximum inference range is bounded by the longest distance seen during training on the dataset. Beyond that distance, state resetting becomes necessary. If you observe pose collapse, switch to windowed mode (--mode windowed

) — in most cases tuning--keyframe_interval

alone is enough and the rest of the windowed parameters can stay at their defaults.

python demo.py --model_path /path/to/lingbot-map-long.pt \
    --video_path video.mp4 --fps 10 \
    --mode windowed --window_size 128 --overlap_keyframes 16 --keyframe_interval 2

Sky masking uses an ONNX sky segmentation model to filter out sky points from the reconstructed point cloud, which improves visualization quality for outdoor scenes.

Setup:

pip install onnxruntime        # CPU
pip install onnxruntime-gpu    # GPU (faster for large image sets)

The sky segmentation model (skyseg.onnx

) will be automatically downloaded from HuggingFace on first use.

Usage:

python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/ --mask_sky

Sky masks are cached in <image_folder>_sky_masks/

so subsequent runs skip regeneration. You can also specify a custom cache directory with --sky_mask_dir

, or save side-by-side mask visualizations with --sky_mask_visualization_dir

:

python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/ --mask_sky \
    --sky_mask_dir /path/to/cached_masks/ \
    --sky_mask_visualization_dir /path/to/mask_viz/

Argument	Default	Description
--port
8080
Viser viewer port
--conf_threshold
1.5
Visibility threshold for filtering low-confidence points
--point_size
0.00001
Point cloud point size
--downsample_factor
10
Spatial downsampling for point cloud display

python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/ --use_sdpa

If you run into out-of-memory issues, try one (or both) of the following:

— offload per-frame predictions to CPU during inference (on by default; use--offload_to_cpu

--no-offload_to_cpu

only if you have memory to spare).— reduce the number of bidirectional scale frames from the default 8 down to 2, which shrinks the activation peak of the initial scale phase.--num_scale_frames 2

Lower the number of iterative refinement steps in the camera head to trade a small amount of pose accuracy for wall-clock speed:

python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/ --camera_num_iterations 1

--camera_num_iterations

defaults to 4

; setting it to 1

skips three refinement passes in the camera head (and shrinks its KV cache by 4×).

Use this pipeline when your sequence is too long for the interactive viser viewer — for example, the indoor walkthrough featured above. demo_render/batch_demo.py

is the all-in-one offline entry point: feed it a video or a folder of images and it will run model inference and produce a headless point-cloud flythrough MP4 in a single command. It shares the same PyTorch / FlashInfer / checkpoint stack as demo.py

.

For those constrained by limited VRAM or GPU usage, you may also refer to the implementation at: https://github.com/ureeey/lingbot-map-rtx4060-8g/commit/eeee84a89cc97c1e39b736b46df4ee315275700b

1. Rendering Python dependencies

pip install -e ".[vis,render]"

render

pulls in open3d>=0.19

and pyyaml

(the core numpy<2

constraint comes from the base lingbot-map

install). Sky masking in this pipeline uses onnxruntime-gpu

for batched segmentation; install it if you don't already have the CPU onnxruntime

:

pip install onnxruntime-gpu

2. Kaolin — matches the PyTorch 2.8.0 + CUDA 12.8 recommended above:

pip install --index-url https://pypi.org/simple \
    kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.8.0_cu128.html

--index-url https://pypi.org/simple

bypasses any internal mirror that might otherwise serve the PyPI placeholder wheel (which raisesImportError

on import). NVIDIA Kaolin does not publish prebuilt wheels for PyTorch 2.9.x — if you're on 2.9 for other reasons, build Kaolin from source (pip install --no-build-isolation git+https://github.com/NVIDIAGameWorks/kaolin.git

, needs local CUDA toolkit). For other torch/CUDA combinations see[NVIDIA Kaolin installation].

3. ffmpeg

sudo apt install ffmpeg    # or: brew install ffmpeg

4. CUDA extensions (required before first run)

cd demo_render/render_cuda_ext && python setup.py build_ext --inplace && cd ../..

This builds voxel_morton_ext

and frustum_cull_ext

in place — both are imported by rgbd_render

for GPU voxelization and frustum culling.

Dataset: Download the example video from robbyant/lingbot-map-demo on Hugging Face.

    python demo_render/batch_demo.py \
    --video_path /data/demo_videos/indoor_travel.MP4 \
    --output_folder /data/outputs/indoor_travel/ \
    --model_path /path/to/lingbot-map.pt \
    --config demo_render/config/indoor.yaml \
    --mode windowed --window_size 128 \
    --keyframe_interval 10 --overlap_keyframes 8 \
    --sky_mask_dir /data/outputs/sky_masks \
    --sky_mask_visualization_dir /data/outputs/sky_mask_viz \
    --camera_vis default --keyframes_only_points \
    --frame_tag --frame_tag_position top_right \
    --save_predictions

Flag-by-flag rationale:

Flag	Why it's there
--mode windowed --window_size 128
Sliding-window inference is required once the sequence exceeds the ~320-frame RoPE training range; each window resets the KV cache. — the first window_size counts KV-cache slots, not actual framesnum_scale_frames (=8) slots hold the scale frames and the remaining 128 − 8 = 120 slots hold keyframes. With keyframe_interval = 13 , one window therefore covers 8 + 120 × 13 = 1568 actual frames.
--keyframe_interval 10
Cache only every 10th frame as a keyframe. Non-keyframes still emit per-frame predictions but don't grow the KV cache
--overlap_keyframes 8
Adjacent windows share 8 keyframes of context, resolved internally to max(num_scale_frames, 8 × keyframe_interval) = 8 × 13 = 104 actual frames of overlap. Recommended whenever keyframe_interval > 1 , to keep cross-window pose alignment stable.
--config demo_render/config/indoor.yaml
Seed render/scene/camera/overlay defaults from the indoor preset (short depth, tighter follow cam). Any CLI flag the user explicitly passes still overrides the YAML value.
--sky_mask_dir / --sky_mask_visualization_dir
Persist sky masks and their side-by-side visualizations to disk so subsequent reruns reuse them instead of re-running ONNX segmentation. (The render pipeline only consumes them when sky masking is enabled — by the YAML preset or by --mask_sky .)
--camera_vis default
Overlay the trajectory trail + recent-frame points on the rendered video.
--keyframes_only_points
Only unproject keyframe depth into the point cloud; non-keyframes still contribute their pose to the trajectory/frustum overlay. Keeps the cloud sparse for very long sequences.
--frame_tag --frame_tag_position top_right
Stamp a <i> / <N> Frames counter in the top-right corner of the MP4.
--save_predictions
Persist per-frame NPZs alongside the MP4. Useful for inspection or for re-rendering with different camera/overlay settings later.

Replacing keyframe_interval = 10 with image_stride = 10 speeds up rendering. Then, comment out the camera follow section in demo_render/config/indoor.yaml and set the birdeye's ranges to [2000, 2500] to reproduce the indoor fly-through effect shown in the demo:

Dataset: Download the example video from robbyant/lingbot-map-demo on Hugging Face.

    python demo_render/batch_demo.py \
    --video_path /data/demo_videos/drive_frames.mp4 \
    --output_folder /data/outputs/drive/ \
    --model_path /path/to/lingbot-map.pt \
    --config demo_render/config/outdoor_drive.yaml \
    --mode windowed --window_size 128 \
    --max_non_keyframe_gap 100 --overlap_keyframes 8 \
    --image_stride 1 \
    --sky_mask_dir /data/outputs/sky_masks \
    --sky_mask_visualization_dir /data/outputs/sky_mask_viz \
    --camera_vis default --keyframes_only_points \
    --frame_tag --frame_tag_position top_right \
    --save_predictions

What differs from the indoor walkthrough above:

Flag	Why it's there
--config demo_render/config/outdoor_drive.yaml
Seed defaults from the outdoor preset: sky masking enabled, deeper render range (max_depth: 250 ), and a follow cam tuned for vehicle trajectories with a final birdeye reveal.
--image_stride 1
Use every video frame. Increase it to subsample long or high-FPS drive footage.
--max_non_keyframe_gap 100
Upper bound on consecutive non-keyframes before a keyframe is forced. Only active with flow-based keyframe selection (--flow_threshold > 0 ); in the default fixed-interval mode it has no effect.

The remaining flags (--mode windowed --window_size 128

, --overlap_keyframes 8

, sky-mask caching, overlays, --save_predictions

) carry over unchanged from the indoor example — see the flag-by-flag table above.

Reconstruct videos generated by LingBot-World, our world model — the same pipeline works on generated footage out of the box.

Dataset: Download the example videos (lingbo_world_frames.mp4

, lingbo_world2_frames.mp4

) from robbyant/lingbot-map-demo on Hugging Face.

    python demo_render/batch_demo.py \
    --video_path /data/demo_videos/lingbo_world_frames.mp4 \
    --output_folder /data/outputs/lingbo_world/ \
    --model_path /path/to/lingbot-map.pt \
    --config demo_render/config/outdoor_drive.yaml \
    --mode windowed --window_size 128 \
    --max_non_keyframe_gap 100 --overlap_keyframes 8 \
    --image_stride 1 \
    --sky_mask_dir /data/outputs/sky_masks \
    --sky_mask_visualization_dir /data/outputs/sky_mask_viz \
    --camera_vis default --keyframes_only_points \
    --frame_tag --frame_tag_position top_right \
    --save_predictions

For the second clip, run the same command with --video_path /data/demo_videos/lingbo_world2_frames.mp4 --output_folder /data/outputs/lingbo_world2/

(and separate --sky_mask_dir

/ --sky_mask_visualization_dir

folders if you want to keep the cached masks apart).

All flags are identical to the outdoor drive scene above — only the input video and output folder change. See the drive scene and indoor walkthrough tables for the flag-by-flag rationale.

The virtual camera path is described by the camera.segments

list in the YAML preset passed via --config

. Edit the YAML to design your own shot — no need to touch CLI flags.

Built-in presets live in demo_render/config/

: default.yaml

, indoor.yaml

, outdoor_drive.yaml

. Copy one and edit the camera:

block.

camera:
  fov: 60.0          # camera field of view in degrees
  transition: 30     # frames blended between adjacent segments
  segments:
    - mode: follow            # chase cam following the input trajectory
      frames: [0, 1500]       # rendered-frame range this segment covers (-1 = end)
      back_offset: 0.3        # how far behind the input camera (fraction of scene scale)
      up_offset: 0.08         # vertical lift above the input camera
      look_offset: 0.4        # how far ahead the lookat target points
      smooth_window: 30       # trajectory smoothing window in frames
    - mode: birdeye           # rise up for a top-down reveal of the whole scene
      frames: [1500, 1800]
      reveal_height_mult: 2.5 # birdeye height = scene scale × this factor
    - mode: follow            # drop back into chase cam
      frames: [1800, -1]
      back_offset: 0.3
      up_offset: 0.08
      look_offset: 0.4

transition

controls how many frames are blended between adjacent segments; frames: [0, -1]

means "the whole sequence".

mode | Behavior | Tunable fields | |---|---|---| follow | Chase cam tracks the input trajectory with smooth offsets. The most cinematic option for walkthroughs. | back_offset , up_offset , look_offset , smooth_window , scale_frames | birdeye | Top-down reveal of the whole scene. Useful for hero / overview shots. | reveal_height_mult | static | Fixed eye + lookat, auto-derived from the segment's start frame. | — | pivot | Fixed eye, lookat sweeps along the trajectory. | — |

Pure follow (most common):

camera:
  fov: 60.0
  segments:
    - mode: follow
      frames: [0, -1]
      back_offset: 0.3
      up_offset: 0.08
      look_offset: 0.4
      smooth_window: 30

Full birdeye (good for overview / hero shots):

camera:
  fov: 60.0
  segments:
    - mode: birdeye
      frames: [0, -1]
      reveal_height_mult: 2.5

Follow with birdeye inserts: just list multiple segments in order under segments:

— adjacent segments are interpolated using transition

frames.

Caveat: when

--config

loads a YAML preset, passinganysegment-shaping CLI flag (--camera_mode

,--back_offset

,--up_offset

,--look_offset

,--smooth_window

,--follow_scale_frames

,--birdeye_start

,--birdeye_duration

,--reveal_height_mult

) discards the YAML'ssegments

and rebuilds the camera path from those flags instead. To stay fully YAML-driven, don't pass any of them on the command line.

For a given output name (e.g. <scene>

or <video_name>

):

File	Description
<name>_pointcloud.mp4
Rendered point-cloud flythrough
<name>_pointcloud_rgb.mp4
Original RGB frames encoded as video
<name>_pointcloud_config.yaml
Full config snapshot of this run
batch_results.json
Per-scene success / duration summary

This project is released under the Apache License 2.0. See LICENSE file for details.

@article{chen2026geometric,
  title={Geometric Context Transformer for Streaming 3D Reconstruction},
  author={Chen, Lin-Zhuo and Gao, Jian and Chen, Yihang and Cheng, Ka Leong and Sun, Yipengjing and Hu, Liangxiao and Xue, Nan and Zhu, Xing and Shen, Yujun and Yao, Yao and Xu, Yinghao},
  journal={arXiv preprint arXiv:2604.14141},
  year={2026}
}

We thank Shangzhan Zhang, Jianyuan Wang, Yudong Jin, Christian Rupprecht, and Xun Cao for their helpful discussions and support.

This work builds upon several excellent open-source projects:

source & further reading

github.com — original article