# LingBot-Map Pretrained Models: `lingbot-map` vs `lingbot-map-long` Comparison

> Explore lingbot-map vs lingbot-map-long pretrained models from Robbyant/lingbot-map. Discover which model suits your video analysis needs for short or long sequences.

- Repository: [Robbyant/lingbot-map](https://github.com/Robbyant/lingbot-map)
- Tags: comparison
- Published: 2026-07-29

---

**The Robbyant/lingbot-map repository provides two official pretrained checkpoints—`lingbot-map` for balanced short and long video performance and `lingbot-map-long` optimized specifically for extended sequences and large-scale scenes—both available as standalone `.pt` files from Hugging Face and ModelScope.**

The LingBot-Map open-source codebase ships with distinct pretrained model variants designed to handle different video reconstruction scenarios. Understanding the differences between the balanced `lingbot-map` checkpoint and the long-sequence `lingbot-map-long` variant ensures optimal 3D reconstruction results across both short clips and massive outdoor drive sequences.

## Available Pretrained LingBot-Map Models

According to the model-download table in the repository's README (lines 135-138), two standalone checkpoints are officially distributed:

### lingbot-map: The Balanced Checkpoint

This is the **default recommended model** used in the original paper and standard benchmarks. The `lingbot-map.pt` file provides balanced performance across both short videos and moderately long sequences, making it the versatile choice for general-purpose 3D reconstruction tasks.

- **Hugging Face**: `robbyant/lingbot-map`
- **ModelScope**: `Robbyant/lingbot-map`
- **File**: `lingbot-map.pt`

### lingbot-map-long: Long-Sequence Optimization

The `lingbot-map-long.pt` checkpoint is specifically **optimized for long sequences and large-scale scenes**. Use this variant when processing very long indoor walkthroughs or extended outdoor drives where memory management and temporal consistency across thousands of frames become critical.

- **Hugging Face**: `robbyant/lingbot-map` (same repository, different file)
- **ModelScope**: `Robbyant/lingbot-map`
- **File**: `lingbot-map-long.pt`

## Downloading the Checkpoints from Hugging Face

Both models are distributed as **stand-alone `.pt` files** that do not require additional configuration files. Download them directly using standard HTTP tools:

```bash

# Download the balanced checkpoint (recommended default)

wget https://huggingface.co/robbyant/lingbot-map/resolve/main/lingbot-map.pt \
     -O /path/to/lingbot-map.pt

```

```bash

# Download the long-sequence-optimized checkpoint

wget https://huggingface.co/robbyant/lingbot-map/resolve/main/lingbot-map-long.pt \
     -O /path/to/lingbot-map-long.pt

```

Both files can also be retrieved from ModelScope using the equivalent URLs in the `Robbyant/lingbot-map` repository.

## Running Inference with Different Model Variants

The demo scripts accept the `--model_path` argument to specify which checkpoint to load. Pass the absolute path to your downloaded `.pt` file to override any default behavior.

### Standard Short-Video Reconstruction

Use the balanced `lingbot-map.pt` for typical reconstruction tasks:

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

```

### Extended Sequence Processing (25K+ Frames)

For very long indoor walkthroughs or massive datasets, switch to the long-optimized checkpoint and enable windowed processing mode:

```bash
python demo.py \
    --model_path /path/to/lingbot-map-long.pt \
    --video_path /data/indoor_walkthrough.mp4 \
    --mode windowed --window_size 128 --keyframe_interval 2 \
    --mask_sky

```

### Offline Batch Rendering

The [`demo_render/batch_demo.py`](https://github.com/Robbyant/lingbot-map/blob/main/demo_render/batch_demo.py) pipeline also supports model selection via `--model_path`, as implemented in the repository's offline rendering utilities:

```bash
python demo_render/batch_demo.py \
    --video_path /data/drive.mp4 \
    --output_folder /data/outputs/drive/ \
    --model_path /path/to/lingbot-map-long.pt \
    --config demo_render/config/outdoor_drive.yaml \
    --mode windowed --window_size 128 --keyframe_interval 10 \
    --mask_sky

```

## Model Loading Architecture

All inference scripts rely on the same loading routine defined in [`lingbot_map/models/gct_stream.py`](https://github.com/Robbyant/lingbot-map/blob/main/lingbot_map/models/gct_stream.py). This file contains the **core streaming model class** (`GCTStream`) that restores the architecture and weights from the saved checkpoint.

When you specify `--model_path`, the demo scripts instantiate `GCTStream` and load the state dictionary from the `.pt` file, rebuilding the full streaming architecture regardless of which variant (balanced or long) you select. This design ensures API consistency between both `lingbot-map` and `lingbot-map-long` checkpoints.

## Summary

- **Two official variants**: `lingbot-map.pt` (balanced) and `lingbot-map-long.pt` (long sequences)
- **Distribution**: Both hosted on Hugging Face and ModelScope under `robbyant/lingbot-map`
- **File format**: Stand-alone PyTorch `.pt` files requiring no additional configuration
- **Loading mechanism**: Unified through [`lingbot_map/models/gct_stream.py`](https://github.com/Robbyant/lingbot-map/blob/main/lingbot_map/models/gct_stream.py) via the `--model_path` argument
- **Selection criteria**: Use `lingbot-map` for general tasks; use `lingbot-map-long` for videos exceeding several thousand frames or large-scale outdoor scenes

## Frequently Asked Questions

### What is the difference between lingbot-map and lingbot-map-long?

The `lingbot-map` checkpoint provides balanced performance suitable for both short and long videos, serving as the default model used in paper benchmarks. The `lingbot-map-long` variant is specifically optimized for extended sequences and large-scale scenes, featuring architectural adjustments that improve stability when processing 25,000+ frames.

### Where are the LingBot-Map pretrained models hosted?

Both checkpoints are available from Hugging Face at `huggingface.co/robbyant/lingbot-map` and from ModelScope at `modelscope.cn/models/Robbyant/lingbot-map`. They are distributed as direct-download `.pt` files rather than Hugging Face Transformers-style repositories.

### How do I load a custom checkpoint in the demo scripts?

Pass the absolute path to your downloaded `.pt` file using the `--model_path` argument in either [`demo.py`](https://github.com/Robbyant/lingbot-map/blob/main/demo.py) or [`demo_render/batch_demo.py`](https://github.com/Robbyant/lingbot-map/blob/main/demo_render/batch_demo.py). The scripts automatically detect the file and load it through the `GCTStream` class defined in [`lingbot_map/models/gct_stream.py`](https://github.com/Robbyant/lingbot-map/blob/main/lingbot_map/models/gct_stream.py).

### Which model should I use for processing very long video sequences?

Use `lingbot-map-long.pt` for sequences exceeding several thousand frames, particularly for large-scale outdoor drives or extended indoor walkthroughs. Combine this checkpoint with windowed processing mode (`--mode windowed --window_size 128`) to manage memory efficiently across long temporal windows.