merge

nerfstudio/fields/nerfacto_field.py

@@ -114,7 +114,7 @@ class TCNNNerfactoField(Field):
         self.use_semantics = use_semantics
 
         num_levels = 16
-        max_res = 1024
+        max_res = 2048
         base_res = 16
         log2_hashmap_size = 19
         features_per_level = 2

redwoods_path.json

@@ -0,0 +1 @@
+{"keyframes": [], "render_height": 720, "render_width": 1280, "camera_path": [{"camera_to_world": [0.42693599492361145, -0.2335212475600636, 0.8736094568951149, 0.5591163096709275, 0.904281845576138, 0.11025171703998835, -0.4124547280019327, -0.2639739801425407, -8.326672684688674e-17, 0.9660809416543348, 0.2582394512313604, 0.16527509843710225, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4306065513226966, -0.23294649819329583, 0.871903754394207, 0.5577727660870201, 0.9025397486634902, 0.1111332174416496, -0.41599177394183684, -0.2661175402119892, 6.357491869934731e-06, 0.9661186971384768, 0.2583305771410153, 0.16525879129504714, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4342651552812074, -0.23237672383565414, 0.870188474929794, 0.5564445314973131, 0.9007850879897202, 0.11201422495277268, -0.4195183145354924, -0.26826219688220787, 1.2803940081116035e-05, 0.9661541655593261, 0.25841130367818427, 0.1652418538643804, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4379118399174736, -0.2318116543593254, 0.8684635822907535, 0.5551308001652699, 0.8990179197808735, 0.11289475612487512, -0.4230343675327209, -0.2704078924373667, 1.9312266814243155e-05, 0.9661874201948074, 0.25848197252594285, 0.1652243193181868, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4415466577667737, -0.23125101914738344, 0.8667290295829195, 0.5538307663543535, 0.8972382896123233, 0.11377482774901562, -0.42653996778270226, -0.2725545691616357, 2.5856220259226068e-05, 0.9662185336216536, 0.2585429271169145, 0.16520622082955116, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4451696804684854, -0.2306945471098336, 0.8649847590236047, 0.552543624328027, 0.8954462320771439, 0.11465445685303727, -0.43003516697543703, -0.2747021693391849, 3.241037134252942e-05, 0.9662475777240137, 0.2585945123572509, 0.16518759157155832, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4487809984411119, -0.23014196669892098, 0.8632307017429565, 0.5512685683497535, 0.8936417704657094, 0.11553366069881739, -0.4335200333729665, -0.27685063525418413, 3.8950110190198706e-05, 0.9662746237015041, 0.25863707435838534, 0.16516846471729316, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4523807205462972, -0.2295930059237089, 0.8614667775917729, 0.5500047926829962, 0.8918249164562299, 0.11641245677951856, -0.43699465153120537, -0.27899990919080353, 4.545164237003649e-05, 0.9662997420766875, 0.25867096017676083, 0.1651488734398404, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4559689737429142, -0.22904739236393662, 0.8596928949553938, 0.5487514915912182, 0.8899956698159017, 0.11729086281683661, -0.4404591220132242, -0.2811499334332131, 5.1891984956417725e-05, 0.966323002701976, 0.258696517561706, 0.16512885091228488, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.459545902732282, -0.2285048531831648, 0.8579089505732519, 0.5475078593378828, 0.8881540181123037, 0.11816889675824047, -0.44391356109479174, -0.2833006502655827, 5.824896246176176e-05, 0.9663444747659505, 0.2587140947115989, 0.16510843030771147, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4631116695955316, -0.22796511514121617, 0.856114829363656, 0.5462730901864528, 0.8862999364346328, 0.11904657677419915, -0.44735810046296104, -0.2854520019720825, 6.45012026773278e-05, 0.9663642267990867, 0.25872404003843485, 0.165087644799205, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.466666453424106, -0.22742790460591955, 0.8543104042533575, 0.5450463784003916, 0.8844333871243405, 0.11992392125539084, -0.4507928869084566, -0.28760393083688235, 7.062813246692041e-05, 0.9663823266788832, 0.25872670194087927, 0.16506652755985032, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4702104499443422, -0.22689294756416556, 0.8524955360114402, 0.5438269182431622, 0.8825543195146978, 0.12080094880988929, -0.45421808201259267, -0.28975637914415225, 7.660997355650708e-05, 0.9663988416343819, 0.2587224285858668, 0.16504511176273218, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.47374387113704103, -0.22635996963228144, 0.8506700730870551, 0.5426139039782278, 0.8806626696787817, 0.12167767826032294, -0.45763386182941634, -0.29190928917806225, 8.242773836150982e-05, 0.966413838250079, 0.25871156769877707, 0.16502343058093544, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.47726694485289145, -0.22582869606573358, 0.848833851450512, 0.5414065298690515, 0.8787583601853555, 0.12255412864100139, -0.46104041656374295, -0.2940626032227824, 8.806322589485542e-05, 0.9664273824692134, 0.2586944663621943, 0.16500151718754494, 0, 0, 0, 1], "fov": 75, "aspect": 1}, {"camera_to_world": [0.4807799144245693, -0.22529885176816475, 0.8469866944372305, 0.5402039901790964, 0.8768412998620887, 0.1234303191950044, -0.4644379502457123, -0.29621626356248243, 9.349901779633234e-05, 0.9664395395964334, 0.25867147082323294, 0.16497940475564546, 0, 0, 0, 1], "fov": 75Line truncated

scripts/process_data.py

@@ -676,6 +676,10 @@ class ProcessImages:
     """Path to the output directory."""
     camera_type: Literal["perspective", "fisheye"] = "perspective"
     """Camera model to use."""
+    sfm_method: Literal["colmap", "OpenSfM"] = "OpenSfM"
+    """Which sfm solver to use"""
+    opensfm_dir: Optional[Path] = None
+    """If sfm_method is OpenSfM, specify the executable to use here"""
     matching_method: Literal["exhaustive", "sequential", "vocab_tree"] = "vocab_tree"
     """Feature matching method to use. Vocab tree is recommended for a balance of speed and
         accuracy. Exhaustive is slower but more accurate. Sequential is faster but should only be used for videos."""
@@ -707,32 +711,51 @@ class ProcessImages:
         # Downscale images
         summary_log.append(downscale_images(image_dir, self.num_downscales, verbose=self.verbose))
 
-        # Run COLMAP
-        colmap_dir = self.output_dir / "colmap"
-        if not self.skip_colmap:
-            colmap_dir.mkdir(parents=True, exist_ok=True)
+        if self.sfm_method == "colmap":
+            # Run COLMAP
+            colmap_dir = self.output_dir / "colmap"
+            if not self.skip_colmap:
+                colmap_dir.mkdir(parents=True, exist_ok=True)
 
-            run_colmap(
-                image_dir=image_dir,
-                colmap_dir=colmap_dir,
-                camera_model=CAMERA_MODELS[self.camera_type],
-                gpu=self.gpu,
-                verbose=self.verbose,
-                matching_method=self.matching_method,
-            )
-
-        # Save transforms.json
-        if (colmap_dir / "sparse" / "0" / "cameras.bin").exists():
-            with CONSOLE.status("[bold yellow]Saving results to transforms.json", spinner="balloon"):
-                num_matched_frames = colmap_to_json(
-                    cameras_path=colmap_dir / "sparse" / "0" / "cameras.bin",
-                    images_path=colmap_dir / "sparse" / "0" / "images.bin",
-                    output_dir=self.output_dir,
+                run_colmap(
+                    image_dir=image_dir,
+                    colmap_dir=colmap_dir,
                     camera_model=CAMERA_MODELS[self.camera_type],
+                    gpu=self.gpu,
+                    verbose=self.verbose,
+                    matching_method=self.matching_method,
+                )
+
+            # Save transforms.json
+            if (colmap_dir / "sparse" / "0" / "cameras.bin").exists():
+                with CONSOLE.status("[bold yellow]Saving results to transforms.json", spinner="balloon"):
+                    num_matched_frames = colmap_to_json(
+                        cameras_path=colmap_dir / "sparse" / "0" / "cameras.bin",
+                        images_path=colmap_dir / "sparse" / "0" / "images.bin",
+                        output_dir=self.output_dir,
+                        camera_model=CAMERA_MODELS[self.camera_type],
+                    )
+                    summary_log.append(f"Colmap matched {num_matched_frames} images")
+            else:
+                CONSOLE.log(
+                    "[bold yellow]Warning: could not find existing COLMAP results. Not generating transforms.json"
+                )
+        elif self.sfm_method == "OpenSfM":
+            assert self.opensfm_dir is not None, "Please provide the path to the OpenSfM directory <...>/bin/opensfm"
+            # Run the OpenSfM solver
+            opensfm_dir = self.output_dir / "opensfm"
+            opensfm_dir.mkdir(parents=True, exist_ok=True)
+            run_opensfm(image_dir, opensfm_dir, CAMERA_MODELS[self.camera_type], self.opensfm_dir)
+            # Save transforms.json
+            if (opensfm_dir / "reconstruction.json").exists():
+                with CONSOLE.status("[bold yellow]Saving results to transforms.json", spinner="balloon"):
+                    opensfm_to_json(
+                        opensfm_dir / "reconstruction.json", self.output_dir, CAMERA_MODELS[self.camera_type]
+                    )
+            else:
+                CONSOLE.log(
+                    "[bold yellow]Warning: could not find existing OpenSfM results. Not generating transforms.json"
                 )
-                summary_log.append(f"Colmap matched {num_matched_frames} images")
-        else:
-            CONSOLE.log("[bold yellow]Warning: could not find existing COLMAP results. Not generating transforms.json")
 
         CONSOLE.rule("[bold green]:tada: :tada: :tada: All DONE :tada: :tada: :tada:")
 

wandb/settings

@@ -0,0 +1,3 @@
+[default]
+entity = luv
+