Created convert.py

convert.py

@@ -0,0 +1,564 @@
+import tyro
+import tqdm
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from core.options import AllConfigs, Options
+from core.gs import GaussianRenderer
+
+import mcubes
+import nerfacc
+import nvdiffrast.torch as dr
+
+from kiui.mesh import Mesh
+from kiui.mesh_utils import clean_mesh, decimate_mesh
+from kiui.mesh_utils import normal_consistency
+from kiui.op import uv_padding, safe_normalize, inverse_sigmoid
+from kiui.cam import orbit_camera, get_perspective
+from kiui.nn import MLP, trunc_exp
+from kiui.gridencoder import GridEncoder
+
+import gradio as gr
+import spaces
+
+
+def get_rays(pose, h, w, fovy, opengl=True):
+
+    x, y = torch.meshgrid(
+        torch.arange(w, device=pose.device),
+        torch.arange(h, device=pose.device),
+        indexing="xy",
+    )
+    x = x.flatten()
+    y = y.flatten()
+
+    cx = w * 0.5
+    cy = h * 0.5
+    focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy))
+
+    camera_dirs = F.pad(
+        torch.stack(
+            [
+                (x - cx + 0.5) / focal,
+                (y - cy + 0.5) / focal * (-1.0 if opengl else 1.0),
+            ],
+            dim=-1,
+        ),
+        (0, 1),
+        value=(-1.0 if opengl else 1.0),
+    )  # [hw, 3]
+
+    rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1)  # [hw, 3]
+    rays_o = pose[:3, 3].unsqueeze(0).expand_as(rays_d)  # [hw, 3]
+
+    rays_d = safe_normalize(rays_d)
+
+    return rays_o, rays_d
+
+
+# Triple renderer of gaussians, gaussian, and diso mesh.
+# gaussian --> nerf --> mesh
+class Converter(nn.Module):
+    def __init__(self, opt: Options):
+        super().__init__()
+
+        self.opt = opt
+        self.device = torch.device("cuda")
+
+        # gs renderer
+        self.tan_half_fov = np.tan(0.5 * np.deg2rad(opt.fovy))
+        self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32, device=self.device)
+        self.proj_matrix[0, 0] = 1 / self.tan_half_fov
+        self.proj_matrix[1, 1] = 1 / self.tan_half_fov
+        self.proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear)
+        self.proj_matrix[3, 2] = -(opt.zfar * opt.znear) / (opt.zfar - opt.znear)
+        self.proj_matrix[2, 3] = 1
+
+        self.gs_renderer = GaussianRenderer(opt)
+
+        self.gaussians = self.gs_renderer.load_ply(opt.test_path).to(self.device)
+
+        # nerf renderer
+        if not self.opt.force_cuda_rast:
+            self.glctx = dr.RasterizeGLContext()
+        else:
+            self.glctx = dr.RasterizeCudaContext()
+
+        self.step = 0
+        self.render_step_size = 5e-3
+        self.aabb = torch.tensor([-1.0, -1.0, -1.0, 1.0, 1.0, 1.0], device=self.device)
+        self.estimator = nerfacc.OccGridEstimator(
+            roi_aabb=self.aabb, resolution=64, levels=1
+        )
+
+        self.encoder_density = GridEncoder(
+            num_levels=12
+        )  # VMEncoder(output_dim=16, mode='sum')
+        self.encoder = GridEncoder(num_levels=12)
+        self.mlp_density = MLP(self.encoder_density.output_dim, 1, 32, 2, bias=False)
+        self.mlp = MLP(self.encoder.output_dim, 3, 32, 2, bias=False)
+
+        # mesh renderer
+        self.proj = (
+            torch.from_numpy(get_perspective(self.opt.fovy)).float().to(self.device)
+        )
+        self.v = self.f = None
+        self.vt = self.ft = None
+        self.deform = None
+        self.albedo = None
+
+    @torch.no_grad()
+    @spaces.GPU
+    def render_gs(self, pose):
+
+        cam_poses = torch.from_numpy(pose).unsqueeze(0).to(self.device)
+        cam_poses[:, :3, 1:3] *= -1  # invert up & forward direction
+
+        # cameras needed by gaussian rasterizer
+        cam_view = torch.inverse(cam_poses).transpose(1, 2)  # [V, 4, 4]
+        cam_view_proj = cam_view @ self.proj_matrix  # [V, 4, 4]
+        cam_pos = -cam_poses[:, :3, 3]  # [V, 3]
+
+        out = self.gs_renderer.render(
+            self.gaussians.unsqueeze(0),
+            cam_view.unsqueeze(0),
+            cam_view_proj.unsqueeze(0),
+            cam_pos.unsqueeze(0),
+        )
+        image = out["image"].squeeze(1).squeeze(0)  # [C, H, W]
+        alpha = out["alpha"].squeeze(2).squeeze(1).squeeze(0)  # [H, W]
+
+        return image, alpha
+
+    def get_density(self, xs):
+        # xs: [..., 3]
+        prefix = xs.shape[:-1]
+        xs = xs.view(-1, 3)
+        feats = self.encoder_density(xs)
+        density = trunc_exp(self.mlp_density(feats))
+        density = density.view(*prefix, 1)
+        return density
+
+    @spaces.GPU
+    def render_nerf(self, pose):
+
+        pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
+
+        # get rays
+        resolution = self.opt.output_size
+        rays_o, rays_d = get_rays(pose, resolution, resolution, self.opt.fovy)
+
+        # update occ grid
+        if self.training:
+
+            def occ_eval_fn(xs):
+                sigmas = self.get_density(xs)
+                return self.render_step_size * sigmas
+
+            self.estimator.update_every_n_steps(
+                self.step, occ_eval_fn=occ_eval_fn, occ_thre=0.01, n=8
+            )
+            self.step += 1
+
+        # render
+        def sigma_fn(t_starts, t_ends, ray_indices):
+            t_origins = rays_o[ray_indices]
+            t_dirs = rays_d[ray_indices]
+            xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
+            sigmas = self.get_density(xs)
+            return sigmas.squeeze(-1)
+
+        with torch.no_grad():
+            ray_indices, t_starts, t_ends = self.estimator.sampling(
+                rays_o,
+                rays_d,
+                sigma_fn=sigma_fn,
+                near_plane=0.01,
+                far_plane=100,
+                render_step_size=self.render_step_size,
+                stratified=self.training,
+                cone_angle=0,
+            )
+
+        t_origins = rays_o[ray_indices]
+        t_dirs = rays_d[ray_indices]
+        xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
+        sigmas = self.get_density(xs).squeeze(-1)
+        rgbs = torch.sigmoid(self.mlp(self.encoder(xs)))
+
+        n_rays = rays_o.shape[0]
+        weights, trans, alphas = nerfacc.render_weight_from_density(
+            t_starts, t_ends, sigmas, ray_indices=ray_indices, n_rays=n_rays
+        )
+        color = nerfacc.accumulate_along_rays(
+            weights, values=rgbs, ray_indices=ray_indices, n_rays=n_rays
+        )
+        alpha = nerfacc.accumulate_along_rays(
+            weights, values=None, ray_indices=ray_indices, n_rays=n_rays
+        )
+
+        color = color + 1 * (1.0 - alpha)
+
+        color = (
+            color.view(resolution, resolution, 3)
+            .clamp(0, 1)
+            .permute(2, 0, 1)
+            .contiguous()
+        )
+        alpha = alpha.view(resolution, resolution).clamp(0, 1).contiguous()
+
+        return color, alpha
+    
+    @spaces.GPU
+    def fit_nerf(self, iters=512, resolution=128):
+
+        self.opt.output_size = resolution
+
+        optimizer = torch.optim.Adam(
+            [
+                {"params": self.encoder_density.parameters(), "lr": 1e-2},
+                {"params": self.encoder.parameters(), "lr": 1e-2},
+                {"params": self.mlp_density.parameters(), "lr": 1e-3},
+                {"params": self.mlp.parameters(), "lr": 1e-3},
+            ]
+        )
+
+        print("[INFO] fitting nerf...")
+        pbar = tqdm.trange(iters)
+        for i in pbar:
+
+            ver = np.random.randint(-45, 45)
+            hor = np.random.randint(-180, 180)
+            rad = np.random.uniform(1.5, 3.0)
+
+            pose = orbit_camera(ver, hor, rad)
+
+            image_gt, alpha_gt = self.render_gs(pose)
+            image_pred, alpha_pred = self.render_nerf(pose)
+
+            # if i % 200 == 0:
+            #     kiui.vis.plot_image(image_gt, alpha_gt, image_pred, alpha_pred)
+
+            loss_mse = F.mse_loss(image_pred, image_gt) + 0.1 * F.mse_loss(
+                alpha_pred, alpha_gt
+            )
+            loss = loss_mse  # + 0.1 * self.encoder_density.tv_loss() #+ 0.0001 * self.encoder_density.density_loss()
+
+            loss.backward()
+            self.encoder_density.grad_total_variation(1e-8)
+
+            optimizer.step()
+            optimizer.zero_grad()
+
+            pbar.set_description(f"MSE = {loss_mse.item():.6f}")
+
+        print("[INFO] finished fitting nerf!")
+
+    @spaces.GPU
+    def render_mesh(self, pose):
+
+        h = w = self.opt.output_size
+
+        v = self.v + self.deform
+        f = self.f
+
+        pose = torch.from_numpy(pose.astype(np.float32)).to(v.device)
+
+        # get v_clip and render rgb
+        v_cam = (
+            torch.matmul(
+                F.pad(v, pad=(0, 1), mode="constant", value=1.0), torch.inverse(pose).T
+            )
+            .float()
+            .unsqueeze(0)
+        )
+        v_clip = v_cam @ self.proj.T
+
+        rast, rast_db = dr.rasterize(self.glctx, v_clip, f, (h, w))
+
+        alpha = torch.clamp(rast[..., -1:], 0, 1).contiguous()  # [1, H, W, 1]
+        alpha = (
+            dr.antialias(alpha, rast, v_clip, f).clamp(0, 1).squeeze(-1).squeeze(0)
+        )  # [H, W] important to enable gradients!
+
+        if self.albedo is None:
+            xyzs, _ = dr.interpolate(v.unsqueeze(0), rast, f)  # [1, H, W, 3]
+            xyzs = xyzs.view(-1, 3)
+            mask = (alpha > 0).view(-1)
+            image = torch.zeros_like(xyzs, dtype=torch.float32)
+            if mask.any():
+                masked_albedo = torch.sigmoid(
+                    self.mlp(self.encoder(xyzs[mask].detach(), bound=1))
+                )
+                image[mask] = masked_albedo.float()
+        else:
+            texc, texc_db = dr.interpolate(
+                self.vt.unsqueeze(0), rast, self.ft, rast_db=rast_db, diff_attrs="all"
+            )
+            image = torch.sigmoid(
+                dr.texture(self.albedo.unsqueeze(0), texc, uv_da=texc_db)
+            )  # [1, H, W, 3]
+
+        image = image.view(1, h, w, 3)
+        # image = dr.antialias(image, rast, v_clip, f).clamp(0, 1)
+        image = image.squeeze(0).permute(2, 0, 1).contiguous()  # [3, H, W]
+        image = alpha * image + (1 - alpha)
+
+        return image, alpha
+
+    @spaces.GPU
+    def fit_mesh(self, iters=2048, resolution=512, decimate_target=5e4):
+
+        self.opt.output_size = resolution
+
+        # init mesh from nerf
+        grid_size = 256
+        sigmas = np.zeros([grid_size, grid_size, grid_size], dtype=np.float32)
+
+        S = 128
+        density_thresh = 10
+
+        X = torch.linspace(-1, 1, grid_size).split(S)
+        Y = torch.linspace(-1, 1, grid_size).split(S)
+        Z = torch.linspace(-1, 1, grid_size).split(S)
+
+        for xi, xs in enumerate(X):
+            for yi, ys in enumerate(Y):
+                for zi, zs in enumerate(Z):
+                    xx, yy, zz = torch.meshgrid(xs, ys, zs, indexing="ij")
+                    pts = torch.cat(
+                        [xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)],
+                        dim=-1,
+                    )  # [S, 3]
+                    val = self.get_density(pts.to(self.device))
+                    sigmas[
+                        xi * S : xi * S + len(xs),
+                        yi * S : yi * S + len(ys),
+                        zi * S : zi * S + len(zs),
+                    ] = (
+                        val.reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy()
+                    )  # [S, 1] --> [x, y, z]
+
+        print(
+            f"[INFO] marching cubes thresh: {density_thresh} ({sigmas.min()} ~ {sigmas.max()})"
+        )
+
+        vertices, triangles = mcubes.marching_cubes(sigmas, density_thresh)
+        vertices = vertices / (grid_size - 1.0) * 2 - 1
+
+        # clean
+        vertices = vertices.astype(np.float32)
+        triangles = triangles.astype(np.int32)
+        vertices, triangles = clean_mesh(
+            vertices, triangles, remesh=True, remesh_size=0.01
+        )
+        if triangles.shape[0] > decimate_target:
+            vertices, triangles = decimate_mesh(
+                vertices, triangles, decimate_target, optimalplacement=False
+            )
+
+        self.v = torch.from_numpy(vertices).contiguous().float().to(self.device)
+        self.f = torch.from_numpy(triangles).contiguous().int().to(self.device)
+        self.deform = nn.Parameter(torch.zeros_like(self.v)).to(self.device)
+
+        # fit mesh from gs
+        lr_factor = 1
+        optimizer = torch.optim.Adam(
+            [
+                {"params": self.encoder.parameters(), "lr": 1e-3 * lr_factor},
+                {"params": self.mlp.parameters(), "lr": 1e-3 * lr_factor},
+                {"params": self.deform, "lr": 1e-4},
+            ]
+        )
+
+        print("[INFO] fitting mesh...")
+        pbar = tqdm.trange(iters)
+        for i in pbar:
+
+            ver = np.random.randint(-10, 10)
+            hor = np.random.randint(-180, 180)
+            rad = self.opt.cam_radius  # np.random.uniform(1, 2)
+
+            pose = orbit_camera(ver, hor, rad)
+
+            image_gt, alpha_gt = self.render_gs(pose)
+            image_pred, alpha_pred = self.render_mesh(pose)
+
+            loss_mse = F.mse_loss(image_pred, image_gt) + 0.1 * F.mse_loss(
+                alpha_pred, alpha_gt
+            )
+            # loss_lap = laplacian_smooth_loss(self.v + self.deform, self.f)
+            loss_normal = normal_consistency(self.v + self.deform, self.f)
+            loss_offsets = (self.deform**2).sum(-1).mean()
+            loss = loss_mse + 0.001 * loss_normal + 0.1 * loss_offsets
+
+            loss.backward()
+
+            optimizer.step()
+            optimizer.zero_grad()
+
+            # remesh periodically
+            if i > 0 and i % 512 == 0:
+                vertices = (self.v + self.deform).detach().cpu().numpy()
+                triangles = self.f.detach().cpu().numpy()
+                vertices, triangles = clean_mesh(
+                    vertices, triangles, remesh=True, remesh_size=0.01
+                )
+                if triangles.shape[0] > decimate_target:
+                    vertices, triangles = decimate_mesh(
+                        vertices, triangles, decimate_target, optimalplacement=False
+                    )
+                self.v = torch.from_numpy(vertices).contiguous().float().to(self.device)
+                self.f = torch.from_numpy(triangles).contiguous().int().to(self.device)
+                self.deform = nn.Parameter(torch.zeros_like(self.v)).to(self.device)
+                lr_factor *= 0.5
+                optimizer = torch.optim.Adam(
+                    [
+                        {"params": self.encoder.parameters(), "lr": 1e-3 * lr_factor},
+                        {"params": self.mlp.parameters(), "lr": 1e-3 * lr_factor},
+                        {"params": self.deform, "lr": 1e-4},
+                    ]
+                )
+
+            pbar.set_description(f"MSE = {loss_mse.item():.6f}")
+
+        # last clean
+        vertices = (self.v + self.deform).detach().cpu().numpy()
+        triangles = self.f.detach().cpu().numpy()
+        vertices, triangles = clean_mesh(vertices, triangles, remesh=False)
+
+        rotation_matrix = np.array(
+            [[-1, 0, 0], [0, -1, 0], [0, 0, 1]], dtype=np.float32
+        )
+        vertices = vertices @ rotation_matrix.T
+
+        self.v = torch.from_numpy(vertices).contiguous().float().to(self.device)
+        self.f = torch.from_numpy(triangles).contiguous().int().to(self.device)
+        self.deform = nn.Parameter(torch.zeros_like(self.v).to(self.device))
+
+        print("[INFO] finished fitting mesh!")
+
+    # uv mesh refine
+    @spaces.GPU
+    def fit_mesh_uv(
+        self, iters=512, resolution=512, texture_resolution=1024, padding=2
+    ):
+
+        self.opt.output_size = resolution
+
+        # unwrap uv
+        print("[INFO] uv unwrapping...")
+        mesh = Mesh(v=self.v, f=self.f, albedo=None, device=self.device)
+        mesh.auto_normal()
+        mesh.auto_uv()
+
+        self.vt = mesh.vt
+        self.ft = mesh.ft
+
+        # render uv maps
+        h = w = texture_resolution
+        uv = mesh.vt * 2.0 - 1.0  # uvs to range [-1, 1]
+        uv = torch.cat(
+            (uv, torch.zeros_like(uv[..., :1]), torch.ones_like(uv[..., :1])), dim=-1
+        )  # [N, 4]
+
+        rast, _ = dr.rasterize(
+            self.glctx, uv.unsqueeze(0), mesh.ft, (h, w)
+        )  # [1, h, w, 4]
+        xyzs, _ = dr.interpolate(mesh.v.unsqueeze(0), rast, mesh.f)  # [1, h, w, 3]
+        mask, _ = dr.interpolate(
+            torch.ones_like(mesh.v[:, :1]).unsqueeze(0), rast, mesh.f
+        )  # [1, h, w, 1]
+
+        # masked query
+        xyzs = xyzs.view(-1, 3)
+        mask = (mask > 0).view(-1)
+
+        albedo = torch.zeros(h * w, 3, device=self.device, dtype=torch.float32)
+
+        if mask.any():
+            print("[INFO] querying texture...")
+
+            xyzs = xyzs[mask]  # [M, 3]
+
+            # batched inference to avoid OOM
+            batch = []
+            head = 0
+            while head < xyzs.shape[0]:
+                tail = min(head + 640000, xyzs.shape[0])
+                batch.append(
+                    torch.sigmoid(self.mlp(self.encoder(xyzs[head:tail]))).float()
+                )
+                head += 640000
+
+            albedo[mask] = torch.cat(batch, dim=0)
+
+        albedo = albedo.view(h, w, -1)
+        mask = mask.view(h, w)
+        albedo = uv_padding(albedo, mask, padding)
+
+        # optimize texture
+        self.albedo = nn.Parameter(inverse_sigmoid(albedo)).to(self.device)
+
+        optimizer = torch.optim.Adam(
+            [
+                {"params": self.albedo, "lr": 1e-3},
+            ]
+        )
+
+        print("[INFO] fitting mesh texture...")
+        pbar = tqdm.trange(iters)
+        for i in pbar:
+
+            # shrink to front view as we care more about it...
+            ver = np.random.randint(-5, 5)
+            hor = np.random.randint(-15, 15)
+            rad = self.opt.cam_radius  # np.random.uniform(1, 2)
+
+            pose = orbit_camera(ver, hor, rad)
+
+            image_gt, alpha_gt = self.render_gs(pose)
+            image_pred, alpha_pred = self.render_mesh(pose)
+
+            loss_mse = F.mse_loss(image_pred, image_gt)
+            loss = loss_mse
+
+            loss.backward()
+
+            optimizer.step()
+            optimizer.zero_grad()
+
+            pbar.set_description(f"MSE = {loss_mse.item():.6f}")
+
+        print("[INFO] finished fitting mesh texture!")
+
+    @torch.no_grad()
+    @spaces.GPU
+    def export_mesh(self, path):
+
+        mesh = Mesh(
+            v=self.v,
+            f=self.f,
+            vt=self.vt,
+            ft=self.ft,
+            albedo=torch.sigmoid(self.albedo),
+            device=self.device,
+        )
+        mesh.auto_normal()
+        mesh.write(path)
+
+
+opt = tyro.cli(AllConfigs)
+
+# load a saved ply and convert to mesh
+assert opt.test_path.endswith(
+    ".ply"
+), "--test_path must be a .ply file saved by infer.py"
+
+converter = Converter(opt).cuda()
+converter.fit_nerf()
+converter.fit_mesh()
+converter.fit_mesh_uv()
+converter.export_mesh(opt.test_path.replace(".ply", ".glb"))