Wrong rendering result of point cloud

[BostonLobster]

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🐛 Bugs / Unexpected behaviors

I'm trying to use the code from render_colored_point.ipynb to render a point cloud from Scan Net. My procedure is as follows:

1. Read the ply point cloud file and convert to PointCloud instance.
2. Read intrinsic and extrinsic.
3. Create a PerspectiveCameras instance with intrinsics and extrinsics.
4. Render the point cloud.

But the rendering result is very different from the ground truth view image, as shown below:	Rendered	GT

The rendered image is downloaded from jupyter, which is resized, the original size is (1296, 1296). The GT is of (968, 1296).

So you can see the camera is at wrong place! The correct camera pose is shooting to the two screens, but the rendered result is shooting above the room.

I know that the extrinsics of Scan Net is in OpenCV coordinate system, so I check information from https://github.com/facebookresearch/pytorch3d/blob/master/docs/notes/cameras.md and https://github.com/vvvv/VL.OpenCV/wiki/Coordinate-system-conversions-between-OpenCV,-DirectX-and-vvvv. I found that in OpenCV, the x-axis points right, y-axis points down, so I guess I just need to rotate them around z-axis by pi, so that it is aligned with pytorch3d? I tried to add minus to the first two column of R, but got a emtpy rendering result.

Instructions To Reproduce the Issue:

Please include the following (depending on what the issue is):

1. following is my code

   
   import os
   import torch
   import torch.nn.functional as F
   import matplotlib.pyplot as plt
   from skimage.io import imread

Util function for loading point clouds

import numpy as np

Data structures and functions for rendering

from pytorch3d.structures import Pointclouds from pytorch3d.renderer import ( look_at_view_transform, OpenGLOrthographicCameras, PointsRasterizationSettings, PointsRenderer, PointsRasterizer, AlphaCompositor, NormWeightedCompositor, SfMPerspectiveCameras, SfMOrthographicCameras, OpenGLPerspectiveCameras ) import trimesh

Setup

if torch.cuda.is_available(): device = torch.device("cuda:0") torch.cuda.set_device(device) else: device = torch.device("cpu")

pc = trimesh.load('./scene0010_00/scene0010_00_vh_clean.ply')

Load point cloud

verts = torch.Tensor(pc.vertices).to(device)
rgb = torch.Tensor(pc.visual.vertex_colors[:, :3] / 255.).to(device) point_cloud = Pointclouds(points=[verts], features=[rgb])

to_tensor = lambda x: [torch.Tensor(i).unsqueeze(0) for i in x]

Load camera parameters

pose_path = './scene0010_00/pose/0000.txt' intrinsic_path = './scene0010_00/intrinsic/intrinsic_color.txt' extrinsic = np.loadtxt(pose_path)

R, T = to_tensor([extrinsic[:3, :3], extrinsic[:3, -1]]) K = torch.Tensor(np.loadtxt(intrinsic_path)).unsqueeze(0)

image_size = 1296

f_screen = torch.stack([K[:, 0, 0], K[:, 1, 1]], dim=1) p_screen = torch.stack([K[:, 0, 2], K[:, 1, 2]], dim=1)

f_ndc = f_screen 2.0 / image_size p_ndc = - (p_screen - image_size / 2.0) 2.0 / image_size

cameras = SfMPerspectiveCameras(focal_length=f_ndc, principal_point=p_ndc, R=R, T=T, device=device)

Define the settings for rasterization and shading. Here we set the output image to be of size

512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1

and blur_radius=0.0. Refer to raster_points.py for explanations of these parameters.

raster_settings = PointsRasterizationSettings( image_size=image_size, radius = 0.003, points_per_pixel = 10, bin_size=100 )

Create a points renderer by compositing points using an alpha compositor (nearer points

are weighted more heavily). See [1] for an explanation.

renderer = PointsRenderer( rasterizer=PointsRasterizer(cameras=cameras, raster_settings=raster_settings), compositor=AlphaCompositor() )

images = renderer(point_cloud) plt.figure(figsize=(10, 10)) plt.imshow(images[0, ..., :3].cpu().numpy()) plt.grid("off") plt.axis("off");

[gkioxari]

Hi @BostonLobster I think this is an issue of coordinate systems. You need to figure out the world coordinate system of the world system and also the R, T given by ScanNet. I haven't worked with ScanNet a lot but I think that their R, T follows a different convention.

[BostonLobster]

@gkioxari Thanks for your reply. However, as far as I know, the R, T of ScanNet is in OpenCV coordinate system, which is following figure

and pytorch3d is using the coordinate system below

So, by rotating the X-Y plane around z-axis by pi in OpenCV coordinate system, we get pytorch3d coordinate. Anything wrong?

[BostonLobster]

My above understanding is correct, by rotating X-Y plane around z-axis we can get the right coordinate. The wrong rendering result comes from other mistake made elsewhere.

[ZX-Yin]

My above understanding is correct, by rotating X-Y plane around z-axis we can get the right coordinate. The wrong rendering result comes from other mistake made elsewhere.

Hi, have you solved the problem? I've been stuck in this problem for a long time.

[Minisal]

@BostonLobster @JasonYinn

Hello, Did any of you solve this problem? I try to rotating X-Y plane around z-axis, but still don't get the correct rendering result.