Home Work 3: Ananya Bal (abal@andrew.cmu.edu)

Collaborated with FNU Abhimanyu

1.3 Ray sampling

1.4 Point sampling

1.5 Volume rendering

2.2. Loss and training

Box center: (0.249, 0.250, -0.001)

Box side lengths: (2.004, 1.500, 1.503)

2.3 Visualization

3 Optimizing a Neural Radiance Field Visualization

4.1 View Dependence

The increased view dependence reduces the generalization quality of the network because the density becomes a function of the view as well as the color. Adding w earlier in the network leads to more reduction in generalizability as the network prioritizes it more and develops a bigger bias towards view.

4.3 High Resolution Imagery

Result from 9-layer NeRF model with default parameters

I have trained the model 4 times with changes in point samples per ray and network capacity. The results are presented below:

Trial No. of layers in Nerf No. of samples per ray Loss
1 5 128 0.0046
2 9 128 0.0031
3 5 200 0.0066
4 5 128 0.0038

Adding more layers (with Dropout) reduces the loss and gives better results. However, adding more points per ray does not improve the performance.

Trial 1

Trial 2

Trial 3

Trial 4