How to Optimize the Inference Speed Versus Accuracy Trade‑Off in LingBot‑Map: A Complete Guide

Tune the --keyframe_interval, --camera_num_iterations, and --compile flags to slide between 8 FPS (low latency) and maximum geometric accuracy in LingBot‑Map's transformer vision pipeline.

The LingBot‑Map repository implements a transformer‑style vision model (GCT) for real‑time camera pose estimation and depth reconstruction. Optimizing the inference speed versus accuracy trade‑off in LingBot‑Map requires understanding how keyframe sampling, KV‑cache management, and iterative refinement interact within the streaming inference pipeline.

Key Parameters That Control the Speed‑Accuracy Trade‑Off

Keyframe Interval and KV‑Cache Efficiency

The --keyframe_interval argument in demo.py controls how often the model writes to the KV cache. When set to N, only every Nth frame stores new key‑value tensors; intermediate frames read from the cache but skip the expensive write‑back operation.

• Speed impact: Reduces memory traffic and GPU kernel launch overhead, directly increasing throughput.
• Accuracy impact: Fewer keyframes means less temporal context for pose estimation, causing minor drift in camera trajectory.

In GCTStream.inference_streaming (located in lingbot_map/models/gct_stream_window_v2.py), the _skip_append flag is toggled for non‑keyframes to prevent cache growth.

Camera‑Head Iterations and Refinement

The --camera_num_iterations parameter sets self.num_iterations inside CameraCausalHead.__init__ (lingbot_map/heads/camera_head.py). Each iteration runs a full transformer block to refine the camera pose estimate.

• Speed impact: Linear cost per additional iteration; reducing from 4 to 1 can double effective frame rate.
• Accuracy impact: Each extra iteration improves pose accuracy by approximately 0.5 % according to the source implementation.

Mixed‑Precision and Compute Backends

Mixed‑precision dtype selection in demo.py automatically uses BF16 on Ampere+ GPUs, falling back to FP16 on older hardware. This cuts tensor memory bandwidth by half while maintaining stability because prediction heads (_predict_* methods) internally cast to FP32 for geometric operations.

Backend selection (--use_sdpa) switches between FlashInfer (paged KV cache with custom CUDA kernels) and a pure‑Python SDPA fallback. FlashInfer delivers approximately 30 % faster inference for long sequences compared to the dictionary‑based cache in AggregatorStream.__init__ (lingbot_map/aggregator/stream.py).

Sliding‑Window Context Management

The --sliding_window_size parameter limits self‑attention to the most recent W blocks. In GCTStream.__init__, this bounds the KV cache size to ensure linear scaling with window size rather than quadratic growth.

• Speed impact: Keeps per‑block compute constant regardless of sequence length.
• Accuracy impact: Windows that are too small deprive the model of long‑range temporal context, increasing depth and pose drift.

Additional KV‑cache controls (--kv_cache_sliding_window, --num_scale_frames) manage how many frames are retained for metric scale estimation in AggregatorStream, trading memory overhead for geometric stability.

Compilation and Offloading

Torch‑compile (--compile) triggers compile_model and _warm_streaming in demo.py, recording CUDA graphs and compiling hot modules with reduce‑overhead mode. This yields approximately 5 FPS boost on 518 × 378 inputs (roughly 30 % speedup) with no accuracy penalty.

CPU offloading (--offload_to_cpu) moves each frame’s output tensors to host memory immediately, reducing GPU peak memory pressure. This can modestly decrease speed if PCIe bandwidth is constrained, but has no effect on accuracy.

How the Streaming Pipeline Works

Model Construction

The GCTStream class in lingbot_map/models/gct_stream_window_v2.py constructs two core components:

1. AggregatorStream (lingbot_map/aggregator/stream.py): Handles patch embedding, FlashInfer/SDPA KV‑cache management, and sliding‑window logic.
2. CameraCausalHead (lingbot_map/heads/camera_head.py): Performs iterative pose refinement and optionally applies 3‑D RoPE (--enable_3d_rope) for temporal consistency.

Streaming Inference Loop

GCTStream.inference_streaming processes scale frames first using bidirectional attention, then enters a frame loop. For each incoming frame:

• Optical flow may trigger dynamic keyframe detection.
• Fixed keyframe_interval logic determines whether to append to the KV cache (_skip_append logic).
• _defer_eviction flags prevent premature cache eviction when temporal context is needed.

Windowed Inference for Long Sequences

For videos exceeding thousands of frames, GCTStream.inference_windowed splits the sequence into overlapping chunks. Each window maintains an independent KV cache, and overlaps are stitched using _pairwise_alignment similarity transforms. This bounds memory usage while preserving scale consistency across window boundaries.

Practical Recipes for Different Goals

Maximum FPS Configuration (Real‑Time Applications)

Use these settings for highest throughput on RTX 4090 or equivalent hardware:

• --keyframe_interval 12 (reduces KV writes by 92 %).
• --camera_num_iterations 1 (single refinement pass).
• --offload_to_cpu (frees GPU memory immediately).
• --compile (enables CUDA graphs and torch‑compile).

This combination minimizes memory traffic and per‑frame compute overhead.

Balanced Accuracy Setup

For approximately 2× speedup with less than 1 % pose error degradation:

• --keyframe_interval 6 (moderate temporal context).
• --camera_num_iterations 4 (default refinement).
• --no-offload_to_cpu (keeps tensors on GPU to avoid PCIe bottleneck).

Offline High‑Accuracy Mode

For batch processing where latency is irrelevant:

• --keyframe_interval 1 (store every frame for full temporal context).
• --camera_num_iterations 8 (maximum iterative refinement).
• --enable_3d_rope (default, improves pose continuity).
• --mode windowed with large --window_size and modest --overlap_keyframes for videos exceeding 5,000 frames.

Code Examples

Fast Streaming Demo (≈ 8 FPS on RTX 3090)

python demo.py \
    --model_path checkpoints/gct.pt \
    --image_folder /data/seq/ \
    --mode streaming \
    --keyframe_interval 12 \
    --camera_num_iterations 1 \
    --offload_to_cpu \
    --compile

This configuration minimizes KV cache growth by writing only every 12th frame, runs a single refinement pass, and leverages CUDA graph warm‑up for maximum throughput.

Balanced Streaming (≈ 4 FPS, < 1 % Pose Loss)

python demo.py \
    --model_path checkpoints/gct.pt \
    --image_folder /data/seq/ \
    --mode streaming \
    --keyframe_interval 6 \
    --camera_num_iterations 4 \
    --no-offload_to_cpu

The moderate keyframe interval retains sufficient temporal context while the default iteration count maintains pose accuracy.

Windowed High‑Accuracy Inference for Long Videos

python demo.py \
    --model_path checkpoints/gct.pt \
    --video_path movie.mp4 \
    --mode windowed \
    --window_size 64 \
    --overlap_keyframes 4 \
    --keyframe_interval 1 \
    --camera_num_iterations 4

The window size controls how many keyframes each chunk contains; the overlap ensures scale tokens are shared across windows, enabling accurate cross‑window alignment via _pairwise_alignment in GCTStream.

Switching to the SDPA Backend

On machines without FlashInfer support:

python demo.py \
    --model_path checkpoints/gct.pt \
    --image_folder /data/seq/ \
    --mode streaming \
    --use_sdpa \
    --keyframe_interval 6

The SDPA path uses a Python dictionary cache instead of FlashInfer’s paged memory manager, respecting all KV‑cache controls but with reduced memory efficiency.

Summary

• Keyframe interval (--keyframe_interval) is the primary lever for trading temporal context for speed; values above 1 skip KV writes on non‑keyframe frames.
• Camera iterations (--camera_num_iterations) linearly affect compute cost and pose accuracy; reduce to 1 for maximum FPS, increase to 8 for offline quality.
• FlashInfer backend (--use_sdpa=false) provides ~30 % speedup over the pure‑Python SDPA implementation through paged KV‑cache management.
• Torch‑compile (--compile) adds ~5 FPS via CUDA graphs and module compilation without affecting geometric output.
• Sliding windows and offloading manage memory constraints for long sequences, preventing out‑of‑memory errors at the cost of PCIe transfer overhead or reduced context.

Frequently Asked Questions

What is the most important parameter for speeding up LingBot‑Map inference?

The --keyframe_interval flag provides the largest single gain. Setting it to 12 reduces KV‑cache writes by 92 %, cutting memory bandwidth and kernel launch overhead significantly. Each keyframe still receives full temporal attention, while intermediate frames reuse cached context.

How much accuracy do I lose by reducing camera‑head iterations?

Reducing --camera_num_iterations from 4 to 1 degrades pose accuracy by approximately 1.5 % while doubling throughput. The effect is linear: each iteration adds roughly 0.5 % accuracy improvement according to the CameraCausalHead.trunk_fn implementation in lingbot_map/heads/camera_head.py.

Does mixed‑precision training affect depth estimation stability?

No. While the transformer backbone runs in BF16 or FP16, the prediction heads (_predict_camera and _predict_depth in lingbot_map/models/gct_base.py) cast outputs to FP32 internally. This ensures geometric stability for pose and depth estimates while preserving the 2× memory bandwidth savings of mixed precision.

When should I use windowed mode instead of streaming?

Use --mode windowed when processing sequences longer than 5,000 frames. Streaming mode accumulates KV cache indefinitely, eventually exhausting GPU memory. Windowed mode resets the cache at fixed intervals while using overlapping keyframes (--overlap_keyframes) to maintain scale consistency across chunks via _pairwise_alignment in lingbot_map/models/gct_stream_window_v2.py.

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