diff --git a/lidar_pipeline/dtm.py b/lidar_pipeline/dtm.py
index 82b5e98..c298955 100644
--- a/lidar_pipeline/dtm.py
+++ b/lidar_pipeline/dtm.py
@@ -285,10 +285,25 @@ def create_dtm_fast(las_file, basename, dtm_dir, resolution):
         dtm = stat.statistic.T
         dtm = dtm[::-1, :]  # Flip Y so north is at top
 
-        # No interpolation: keep NaN for zones without LiDAR data
+        # Interpolate NaN gaps using distance-weighted nearest-neighbor fill
         nan_count = np.count_nonzero(np.isnan(dtm))
         if nan_count > 0:
-            logger.info(f"    {nan_count:,} pixels sans données (conservés en NaN)")
+            total = dtm.size
+            nan_pct = 100.0 * nan_count / total
+            logger.info(f"    {nan_count:,} pixels sans données ({nan_pct:.1f}%) — interpolation...")
+
+            from rasterio.fill import fillnodata
+            # rasterio.fill.fillnodata uses GDAL's interpolation:
+            # fills gaps from surrounding valid pixels with distance weighting
+            dtm_filled = fillnodata(dtm.astype(np.float32), mask=~np.isnan(dtm),
+                                    max_search_distance=max(width, height) // 4)
+            dtm = dtm_filled.astype(np.float64)
+
+            remaining = np.count_nonzero(np.isnan(dtm))
+            if remaining > 0:
+                logger.warning(f"    {remaining:,} pixels encore sans données après interpolation")
+            else:
+                logger.info(f"    ✓ Interpolation terminée — tous les trous comblés")
 
         # Save as GeoTIFF
         output_tif = dtm_dir / f"{basename}_dtm.tif"
diff --git a/lidar_pipeline/rendering.py b/lidar_pipeline/rendering.py
index 5aadc48..eaae0c0 100644
--- a/lidar_pipeline/rendering.py
+++ b/lidar_pipeline/rendering.py
@@ -324,8 +324,10 @@ def tif_to_png(tif_file, vis_dir, resolution):
             # Apply colormap
             data, cmap, title, legend_label, description, is_rgb_result = _apply_colormap(data, tif_file)
 
-            # Create figure
-            fig_width = 20
+            # Create figure — adapt width to data resolution for sharp rendering
+            # At high res (5000+px wide), we need a larger figure to avoid downsampling artifacts
+            fig_width = max(20, width / 150)
+            fig_width = min(fig_width, 40)  # cap at 40 inches
             map_aspect = height / width
             fig = plt.figure(figsize=(fig_width, fig_width * map_aspect * 0.7 + 2.5),
                             facecolor='white')
@@ -335,9 +337,11 @@ def tif_to_png(tif_file, vis_dir, resolution):
 
             ax = fig.add_subplot(gs[0])
             if is_rgb:
-                im = ax.imshow(data, aspect='equal', origin='upper')
+                im = ax.imshow(data, aspect='equal', origin='upper',
+                               interpolation='bilinear')
             else:
-                im = ax.imshow(data, cmap=cmap, aspect='equal', origin='upper')
+                im = ax.imshow(data, cmap=cmap, aspect='equal', origin='upper',
+                               interpolation='bilinear')
 
             ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
 
@@ -450,9 +454,10 @@ def tif_to_png(tif_file, vis_dir, resolution):
 
             fig.patch.set_facecolor('white')
 
-            # Save as PNG then convert to WebP
+            # Save as PNG then convert to WebP — use higher DPI for large data
+            save_dpi = 200 if width > 3000 else 150
             png_temp = vis_dir / f"{tif_file.stem}_temp.png"
-            plt.savefig(png_temp, dpi=150, bbox_inches='tight', pad_inches=0.15,
+            plt.savefig(png_temp, dpi=save_dpi, bbox_inches='tight', pad_inches=0.15,
                        facecolor='white', format='png')
             plt.close()