Performance optimizations and rendering improvements

GPU multi-processing fix:
- gpu.py: revert to CUDA_VISIBLE_DEVICES approach with lazy CuPy init
  (Device.use() caused CUDA_ERROR_NO_BINARY_FOR_GPU on GPU 1)
- CuPy is imported lazily on first to_gpu() call, allowing
  CUDA_VISIBLE_DEVICES to be set before CUDA context creation
- nvidia-smi used for GPU count detection (no CUDA import needed)
- pipeline.py: add tip message suggesting -w N when multiple GPUs detected

Rendering improvements:
- Title: split into bold title (14pt) + italic description (10pt)
- North arrow: moved inside data area (top-right) with transparent
  background — no longer overlaps title
- Colorbar: full height (compass gap removed), ScalarFormatter with
  useOffset=False to prevent scientific notation on small values

Performance:
- rendering.py: save matplotlib figure to BytesIO instead of temp PNG
  file — eliminates disk I/O between matplotlib and PIL
- visualizations.py: cap max_dist at 300 for ray-tracing (SVF,
  openness, aniso_open) — avoids 500+ iterations at 0.2m resolution
- pipeline.py: deduplicate n_gpus calculation in parallel path

lidar_pipeline/pipeline.py

@@ -447,17 +447,18 @@ class LidarArchaeoPipeline:
         t_pipeline_start = time.time()
 
         if self.workers > 1 and len(files) > 1:
-            n_gpus = num_gpus()
+            n_gpus = num_gpus() or 1
             if n_gpus > 1:
                 logger.info(f"Traitement parallèle avec {self.workers} workers sur {n_gpus} GPUs...")
             else:
                 logger.info(f"Traitement parallèle avec {self.workers} workers...")
+            if n_gpus > 1 and self.workers == 1:
+                logger.info(f"Conseil: utilisez -w {n_gpus} pour exploiter tous les GPUs")
             logger.info(f"Fichiers: {len(files)}")
 
             with ProcessPoolExecutor(max_workers=self.workers) as executor:
                 # Pass resolutions as comma-separated string for multiprocessing serialization
                 resolutions_str = ','.join(str(r) for r in self.resolutions)
-                n_gpus = num_gpus() or 1
                 future_to_file = {
                     executor.submit(_process_file_standalone, str(laz_file), str(self.input_dir), str(self.output_dir), resolutions_str, self.force, self.ground_method, self.force_classify, self.keep_tif, self.quality, self.only_viz, self.skip_viz, self.output_format, gpu_id % n_gpus): laz_file
                     for gpu_id, laz_file in enumerate(files)