ablate_quantized_reconstruction.py

"""
Ablate quantized reconstruction for 3DGS PLY files.

This script builds multiple reconstruction variants from one fine PLY:
  - all:         quantize scale + rotation + dc + sh
  - only_scale:  quantize only scale
  - only_rotation
  - only_dc
  - only_sh
  - no_scale:    keep scale original, quantize the others
  - no_rotation
  - no_dc
  - no_sh

The goal is to visually isolate which quantized attribute introduces
the strongest artifacts such as spikes, blurring, or ghosting.
"""

import argparse
import os
from typing import Dict, Iterable, Set

import numpy as np


FIELD_NAMES = ("scale", "rotation", "dc", "sh")

MODE_MAP: Dict[str, Set[str]] = {
    "all": {"scale", "rotation", "dc", "sh"},
    "only_scale": {"scale"},
    "only_rotation": {"rotation"},
    "only_dc": {"dc"},
    "only_sh": {"sh"},
    "no_scale": {"rotation", "dc", "sh"},
    "no_rotation": {"scale", "dc", "sh"},
    "no_dc": {"scale", "rotation", "sh"},
    "no_sh": {"scale", "rotation", "dc"},
}


def _quantize_or_copy(name: str,
                      features: np.ndarray,
                      codebook: np.ndarray,
                      enabled: bool,
                      quantize_fn) -> dict:
    if enabled:
        idx, recon, stats = quantize_fn(features, codebook, name)
        return {
            "indices": idx,
            "reconstructed": recon.astype(np.float32),
            "stats": stats,
            "quantized": True,
        }

    return {
        "indices": None,
        "reconstructed": features.astype(np.float32, copy=True),
        "stats": None,
        "quantized": False,
    }


def build_variant_results(data: dict,
                          codebooks: dict,
                          quantized_fields: Set[str],
                          quantize_fn) -> dict:
    feature_map = {
        "scale": data["scales"],
        "rotation": data["rotations"],
        "dc": data["dc"],
        "sh": data["sh_rest"],
    }

    results = {}
    for name in FIELD_NAMES:
        results[name] = _quantize_or_copy(
            name=name,
            features=feature_map[name],
            codebook=codebooks[name],
            enabled=(name in quantized_fields),
            quantize_fn=quantize_fn,
        )
    return results


def print_mode_summary(mode: str, results: dict) -> None:
    print(f"\n{'=' * 72}")
    print(f"[mode] {mode}")
    print(f"{'field':<12s} {'quantized':<10s} {'rmse':>12s} {'max_err':>12s} {'usage':>10s}")
    print(f"{'-' * 72}")
    for name in FIELD_NAMES:
        res = results[name]
        if res["stats"] is None:
            print(f"{name:<12s} {'no':<10s} {'-':>12s} {'-':>12s} {'-':>10s}")
        else:
            s = res["stats"]
            print(
                f"{name:<12s} {'yes':<10s} "
                f"{s['rmse']:>12.6f} {s['max_err']:>12.6f} "
                f"{s['cluster_usage']:>10d}"
            )
    print(f"{'=' * 72}")


def parse_args():
    parser = argparse.ArgumentParser(
        description="Generate ablated quantized reconstruction PLY variants."
    )
    parser.add_argument("ply_path", type=str, help="Path to the fine/original PLY.")
    parser.add_argument(
        "--codebook_dir",
        type=str,
        default="./codebooks",
        help="Directory containing scale/rotation/dc/sh codebooks.",
    )
    parser.add_argument(
        "--save_dir",
        type=str,
        default="./ablate_quantized",
        help="Output directory for ablation PLY files.",
    )
    parser.add_argument(
        "--modes",
        nargs="+",
        default=["all", "only_scale", "only_rotation", "only_dc", "only_sh"],
        choices=sorted(MODE_MAP.keys()),
        help="Which ablation variants to write.",
    )
    return parser.parse_args()


def main():
    args = parse_args()
    from quantize import read_ply, load_codebook, quantize, save_reconstructed_ply

    os.makedirs(args.save_dir, exist_ok=True)

    data = read_ply(args.ply_path)

    codebooks = {
        name: load_codebook(args.codebook_dir, name)
        for name in FIELD_NAMES
    }

    scene_name = os.path.splitext(os.path.basename(args.ply_path))[0]

    for mode in args.modes:
        quantized_fields = MODE_MAP[mode]
        results = build_variant_results(data, codebooks, quantized_fields, quantize)
        print_mode_summary(mode, results)

        out_path = os.path.join(args.save_dir, f"{scene_name}_{mode}.ply")
        save_reconstructed_ply(out_path, data, results)
        print(f"[save] {out_path}")


if __name__ == "__main__":
    main()